Network for project assignment: Coachella Artist Analysis¶

In [ ]:
import pandas as pd
import networkx as nx 
import netwulf as nw 

from tqdm import tqdm
import matplotlib.pyplot as plt 
import numpy as np

import time
import datetime
from bs4 import BeautifulSoup
import re
import spotipy
from spotipy.oauth2 import SpotifyClientCredentials
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from textblob import TextBlob
from wordcloud import WordCloud
from collections import Counter
import string

C:\Users\whitl\AppData\Local\Temp\ipykernel_3248\4246152538.py:1: DeprecationWarning: 
Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),
(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)
but was not found to be installed on your system.
If this would cause problems for you,
please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466
        
  import pandas as pd

Section 1: Motivation¶

Dataset¶

We have created 2 datasets for our analysis: 1. Artist Data, 2. Raw Song Data.

1. Artist Data coachella_artists_full.csv

This dataset contains all of the artists that have performed at Coachella, from 1999 to 2023, which stage they performed on and information about each artist (id, music genre, followers, popularity level). We retrieved this information by scraping the Wikipedia page: “Coachella Festival Line-Ups,” and by using the Spotify API.

2. Song Data song_lyrics_dataset.csv

This dataset contains raw lyric data sourced from the MusixMatch API. This API allows for free access to 30% of lyrics for each song with a reasonable rate limit. Due to API limits and the 30% cap on lyrics, we decided to focus only on artists that performed on the Coachella Main Stage, regardless of the year. We searched for the top 5 songs that have lyrics on MusixMatch for each artist, finding a total of 1292 songs that met the criteria.

Note that there are other files in the data/ directory that are used in this project, but they are subsets of the listed datsets.

Why These Datasets?¶

We chose these datasets for a few reasons, mainly that they contain the necessary data that aligns with the goal for our analysis. We are analyzing data from Coachella since its first year in 1999 and the Wikipedia page contains all of the Coachella information needed. Combining the Coachella data with artist data from the Spotify API, we created a completed data set (for our analysis). Furthermore, we know that both our datasets contain accurate data, as it was retrieved from reliable sources, such as Wikipedia, Spotify and MusixMatch. Both datasets are also of desirable size, allowing us to conduct a thorough analysis. Lastly, the data we retrieved was accessible and available for use, another important aspect of a dataset.

Goal for End User¶

Our goal was to provide a cohesive analysis of performing artists at the annual Coachella music festival. In our network analysis, we aimed to illustrate artists that performed several times and whether they gained popularity over the years. The goal of our text analysis was to analyze the most popular words that reoccur in specific artists songs. For this, we focused on the artists that performed on the main Coachella stage, over the 25 years of Coachella data. Overall, the combination of our network and text analysis provides an overall picture of the Coachella lineups for the past 25 years, and >??, …

Data Collection Code¶

Below is the code used to build the artists dataset using the Spotify API and the WikiPedia page for Coachella.

In [ ]:
# Retrieve API given client code
def get_auth_token():
    client_id = ""
    client_secret = ""
    headers = {"Content-Type": "application/x-www-form-urlencoded"}
    data = f"grant_type=client_credentials&client_id={client_id}&client_secret={client_secret}"
    url = "https://accounts.spotify.com/api/token"

    response = requests.post(url=url, data=data, headers=headers)
    token = response.json()["access_token"]
    print(f"TOKEN {token} GENERATED")
    return {"Authorization": f"Bearer  {token}"}

auth = []
auth.append(get_auth_token())

TOKEN BQCEJirmmzSEtgIF-BQ_gChvin_4K22Lv9CtgT4R7VtBSAaT8PJton13h1gEOd_QueUbSk3fYrW0sbfdWNZiQixAUNtbEo_p0tJTEyEyLL5aHluCVEY GENERATED
In [ ]:
# Parse artist info as DF row from JSON response
def extract_artist(artist_json: dict):
    id = artist_json["id"]
    name = artist_json["name"]
    genres = artist_json["genres"]
    followers = artist_json["followers"]
    popularity = artist_json["popularity"]
    return {
        "id": id,
        "name": name,
        "genres": genres,
        "followers": followers,
        "popularity": popularity
        }

# Query API for artist
def get_artist(a_id: str, auth: list):
    artist_url = f"https://api.spotify.com/v1/artists/{a_id}"
    response = requests.get(url=artist_url, headers=auth[-1])

    # check for expired token
    if response.status_code == 401:
        print(f"TOKEN EXPIRED")
        auth.append(get_auth_token())
        return get_artist(a_id, auth)
    return response.json()

def get_related_artists(a_id: str, auth: list):
    related_url = f"https://api.spotify.com/v1/artists/{a_id}/related-artists"
    response = requests.get(url=related_url, headers=auth[-1])
    
    # check for expired token
    if response.status_code == 401:
        print(f"TOKEN EXPIRED")
        auth.append(get_auth_token())
        return get_related_artists(a_id, auth)

    return response.json()   

def extract_related_artists(related_json: dict, visited: set, sources: list):
    artists = []
    for a in related_json["artists"]:
        if a["id"] not in visited:
            visited.add(a["id"])
            sources.append(a["id"])
            artists.append(extract_artist(a))
    return artists
In [ ]:
# These functions are used to collect Spotify song data, not to be confused with the MusixMatch lyric data which is collected later
def get_top_tracks(a_id: str, auth: list):
    url = f"https://api.spotify.com/v1/artists/{a_id}/top-tracks"
    response = requests.get(url, headers=auth[-1])
    
    if response.status_code == 401:
        print(f"TOKEN EXPIRED")
        auth.append(get_auth_token())
        return get_related_artists(a_id, auth[-1])

    # wait for clearance if request is denied for rate limiting reasons
    if response.status_code == 429:
        sleep = int(response.headers["retry-after"]) + 1
        if sleep > 1800:
            raise BaseException(f"API Limited for too long. Pausing. Try again in {sleep} seconds.")
        print(f"Rate limited. Sleeping for {sleep} seconds.")
        time.sleep(sleep)
        return get_top_tracks(a_id, auth)

    if response.status_code == 404:
        raise LookupError('Track not found')
    
    return response.json()

def extract_top_tracks(tracks_json):
    tracks = []
    for track in tracks_json["tracks"]:
        artists = track["artists"]
        duration = track["duration_ms"]
        explicit = track["explicit"]
        id = track["id"]
        name = track["name"]
        popularity = track["popularity"]
        row = {
            "id": id,
            "name": name,
            "artists": artists,
            "duration": duration,
            "explicit": explicit,
            "popularity": popularity
        }
        tracks.append(row)
    return tracks
In [ ]:
# URL of the Wikipedia page
wiki_url = 'https://en.wikipedia.org/wiki/Coachella_Festival_line-ups'

# Fetch the web page content
response = requests.get(wiki_url)
soup = BeautifulSoup(response.content, 'html.parser')

# Find all tables on the page
tables = soup.find_all('table', class_='wikitable')

table_years = [1999, 1999, 2001, 2002, 2002, 2003, 2003, 2004, 2004, 2005, 2005, 2006, 2006, 2007, 2007, 2007, 2008, 2008, 2008, 2009, 2009, 2009, 2010, 2010, 2010, 2011, 2011, 2011, 2012, 2012, 2012, 2013, 2013, 2013, 2014, 2014, 2014, 2015, 2015, 2015, 2016, 2016, 2016, 2017, 2017, 2017, 2018, 2018, 2018, 2019, 2019, 2019, 2020, 2022, 2022, 2022, 2023, 2023, 2023]
performers_data = []

# Iterate through each table and extract its contents
for index, table in enumerate(tables, start=1):
    year = table_years[index - 1]
    if year == 2020:
        continue

    rows = table.find_all('tr')
    header_row = True
    headers = []
    performers = []
    for row in rows:
        cells = row.find_all(['th', 'td'])
        counter = 0
        for cell in cells:
            if header_row:
                headers.append(cell.get_text(strip=True))
            else:
                performers = cell.get_text(strip=False).split("\n")[1:-1]
                performers_data.append({
                    "stage": headers[counter],
                    "year": year,
                    "performers": performers
                    })
                counter += 1
        header_row = False
In [ ]:
# function for name cleaning
# Note that this cleaning is for the wikipedia names, not the names given by Spotify
def remove_parentheses(text):
    pattern = r'\([^)]*\)'
    clean_text = re.sub(pattern, '', text)
    return clean_text.strip()
In [ ]:
# data transformation, creating intermediate tables
performers_df = pd.DataFrame(data=performers_data)
singular = performers_df.explode(column="performers")
singular["name"] = singular["performers"].apply(remove_parentheses)
singular = singular.drop(labels="performers", axis=1)
In [ ]:
# combining the existing table with the performer_ids table
artist_dataset = pd.merge(performer_ids, singular, how="left", on="name")
artist_dataset = artist_dataset.drop_duplicates(subset=["name", "stage", "year"])
artist_dataset = artist_dataset.dropna(subset="id")
artist_dataset = artist_dataset.reset_index().drop(labels='index', axis=1)
artist_dataset
name id stage year
0 Beck 4obzFoKoKRHIphyHzJ35G3 Main Stage 1999
1 Beck 4obzFoKoKRHIphyHzJ35G3 Gobi Tent 2004
2 Beck 4obzFoKoKRHIphyHzJ35G3 Coachella Stage 2014
3 The Chemical Brothers 1GhPHrq36VKCY3ucVaZCfo Main Stage 1999
4 The Chemical Brothers 1GhPHrq36VKCY3ucVaZCfo Insomniac Tent 2001
... ... ... ... ...
2907 Cassian 1ChtRJ3f4rbv4vtz87i6CD Yuma Tent 2023
2908 TSHA 2kLa7JZu4Ijdz1Gle2khZh Yuma Tent 2023
2909 LP Giobbi 3oKnyRhYWzNsTiss5n4Z1J Yuma Tent 2023
2910 Airrica 57sPl5iWgq5t6AscVbMTOW Yuma Tent 2023
2911 Minus the Light 2QKPp6hTeYMCSemGtfAmXx Yuma Tent 2023

2912 rows × 4 columns

In [ ]:
coachella_perf = pd.read_csv("data/coachella_artists.csv")
coachella_artists = coachella_perf.drop_duplicates(subset='id')
id_list = coachella_artists['name'].to_list()
genres = pd.DataFrame(columns=['name', 'genres'])

coachella_artist_meta = []
for id in id_list:
    a_json = get_artist(id, auth)
    row = extract_artist(a_json)
    print(row)
    coachella_artist_meta.append(row)

{'id': '181bsRPaVXVlUKXrxwZfHK', 'name': 'Megan Thee Stallion', 'genres': ['houston rap', 'pop', 'r&b', 'rap', 'trap queen'], 'followers': {'href': None, 'total': 8958891}, 'popularity': 75}
{'id': '1AhjOkOLkbHUfcHDSErXQs', 'name': '88rising', 'genres': ['asian american hip hop'], 'followers': {'href': None, 'total': 972339}, 'popularity': 60}
{'id': '4fxd5Ee7UefO4CUXgwJ7IP', 'name': 'Giveon', 'genres': ['r&b'], 'followers': {'href': None, 'total': 3731383}, 'popularity': 73}
{'id': '4Uc8Dsxct0oMqx0P6i60ea', 'name': 'Conan Gray', 'genres': ['bedroom pop', 'pop', 'pov: indie'], 'followers': {'href': None, 'total': 9552638}, 'popularity': 76}
{'id': '3ycxRkcZ67ALN3GQJ57Vig', 'name': 'Masego', 'genres': ['alternative r&b', 'indie soul'], 'followers': {'href': None, 'total': 1360195}, 'popularity': 69}
{'id': '1gWjcmBsveEYMxOZ0VRi32', 'name': 'Koffee', 'genres': ['dancehall', 'dancehall queen', 'jamaican dancehall', 'modern reggae'], 'followers': {'href': None, 'total': 716570}, 'popularity': 53}
{'id': '5qBZETtyzfYnXOobDXbmcD', 'name': 'Danny Elfman', 'genres': ['orchestral soundtrack', 'soundtrack'], 'followers': {'href': None, 'total': 405374}, 'popularity': 57}
{'id': '2vnB6tuQMaQpORiRdvXF9H', 'name': 'Beach Bunny', 'genres': ['bubblegrunge', 'chicago indie', 'indie pop', 'pov: indie'], 'followers': {'href': None, 'total': 1404257}, 'popularity': 66}
{'id': '5JMLG56F1X5mFmWNmS0iAp', 'name': 'Chelsea Cutler', 'genres': ['alt z'], 'followers': {'href': None, 'total': 710004}, 'popularity': 62}
{'id': '1nVq0hKIVReeaiB3xJgKf0', 'name': 'Molchat Doma', 'genres': ['belarusian indie', 'russian post-punk', 'sovietwave'], 'followers': {'href': None, 'total': 1233440}, 'popularity': 59}
{'id': '7Hvq85OU8T7Hsd63zNBwaL', 'name': 'black midi', 'genres': ['avant-prog', 'brutal prog', 'crank wave', 'noise rock', 'uk noise rock', 'uk post-punk revival'], 'followers': {'href': None, 'total': 256869}, 'popularity': 41}
{'id': '6AQEfqGPSxZX0nJVonYxi6', 'name': 'Inner Wave', 'genres': ['indie garage rock'], 'followers': {'href': None, 'total': 207317}, 'popularity': 46}
{'id': '33yje3hgpNfdXpqdJQcrt9', 'name': 'Mannequin Pussy', 'genres': ['bubblegrunge', 'indie punk', 'modern hardcore', 'modern power pop', 'riot grrrl'], 'followers': {'href': None, 'total': 155112}, 'popularity': 50}
{'id': '2UZIAOlrnyZmyzt1nuXr9y', 'name': 'Nicki Nicole', 'genres': ['argentine hip hop', 'r&b argentino', 'trap argentino', 'trap latino', 'urbano latino'], 'followers': {'href': None, 'total': 6556429}, 'popularity': 76}
{'id': '3JSSjGYcIkgsrz7892CelT', 'name': 'Ed Maverick', 'genres': ['chihuahua indie', 'indie triste', 'mexican indie'], 'followers': {'href': None, 'total': 3190799}, 'popularity': 65}
{'id': '09kXLeOXRyfNQMXRaDO4qA', 'name': 'Nilüfer Yanya', 'genres': ['art pop', 'uk alternative pop'], 'followers': {'href': None, 'total': 174111}, 'popularity': 46}
{'id': '7Aw7RMHmISxyQBftcksDZ3', 'name': 'Beach Goons', 'genres': ['indie garage rock', 'san diego indie', 'surf punk'], 'followers': {'href': None, 'total': 102078}, 'popularity': 40}
{'id': '2h3ooJn8m8X8cL2g1BZ1Rd', 'name': 'Yard Act', 'genres': ['crank wave', 'uk post-punk revival'], 'followers': {'href': None, 'total': 109616}, 'popularity': 47}
{'id': '7Gh6fW49xfXLP8DTWaLVJP', 'name': 'Kyary Pamyu Pamyu', 'genres': ['j-idol', 'japanese electropop'], 'followers': {'href': None, 'total': 464097}, 'popularity': 45}
{'id': '0Y4inQK6OespitzD6ijMwb', 'name': 'Freddie Gibbs', 'genres': ['alternative hip hop', 'hip hop', 'indiana hip hop', 'rap', 'underground hip hop'], 'followers': {'href': None, 'total': 1263169}, 'popularity': 62}
{'id': '6tzRZ39aZlNqlUzQlkuhDV', 'name': 'Pabllo Vittar', 'genres': ['dance pop', 'funk carioca', 'funk pop', 'pop lgbtq+ brasileira', 'pop nacional'], 'followers': {'href': None, 'total': 4129856}, 'popularity': 67}
{'id': '4Ge8xMJNwt6EEXOzVXju9a', 'name': 'Caroline Polachek', 'genres': ['art pop', 'escape room', 'indie pop', 'indietronica'], 'followers': {'href': None, 'total': 498730}, 'popularity': 58}
{'id': '2KEqzdPS7M5YwGmiuPTdr5', 'name': 'Rina Sawayama', 'genres': ['art pop', 'electropop', 'escape room', 'indie pop'], 'followers': {'href': None, 'total': 1017325}, 'popularity': 57}
{'id': '4kIwETcbpuFgRukE8o7Opx', 'name': 'Arlo Parks', 'genres': ['indie pop', 'indie soul', 'uk contemporary r&b'], 'followers': {'href': None, 'total': 604787}, 'popularity': 57}
{'id': '5dFSooV44ddBp3SJ9yVFy4', 'name': 'L’Impératrice', 'genres': [], 'followers': {'href': None, 'total': 221}, 'popularity': 17}
{'id': '0duLKMlcwhyZgqu8zSSjBp', 'name': 'Alaina Castillo', 'genres': ['chill r&b'], 'followers': {'href': None, 'total': 125900}, 'popularity': 47}
{'id': '1Eb3HibZi84vbKz54keKsQ', 'name': 'Mark Lizaola', 'genres': [], 'followers': {'href': None, 'total': 33}, 'popularity': 0}
{'id': '57vWImR43h4CaDao012Ofp', 'name': 'Steve Lacy', 'genres': ['afrofuturism'], 'followers': {'href': None, 'total': 4509376}, 'popularity': 77}
{'id': '3uwAm6vQy7kWPS2bciKWx9', 'name': 'girl in red', 'genres': ['bedroom pop', 'indie pop', 'norwegian indie', 'pov: indie'], 'followers': {'href': None, 'total': 7135277}, 'popularity': 72}
{'id': '0nnYdIpahs41QiZ9MWp5Wx', 'name': 'Holly Humberstone', 'genres': ['alt z', 'indie pop', 'modern alternative pop', 'uk pop'], 'followers': {'href': None, 'total': 242646}, 'popularity': 56}
{'id': '0tbeZu9lv8YEKSQ9tZSslu', 'name': 'Amber Mark', 'genres': ['alternative r&b', 'indie soul', 'nyc pop'], 'followers': {'href': None, 'total': 416794}, 'popularity': 54}
{'id': '0mCTPQ5oa1lbPvbw4kc0eX', 'name': 'Dee Gees', 'genres': [], 'followers': {'href': None, 'total': 13374}, 'popularity': 29}
{'id': '1URnnhqYAYcrqrcwql10ft', 'name': '21 Savage', 'genres': ['atl hip hop', 'hip hop', 'rap'], 'followers': {'href': None, 'total': 18282817}, 'popularity': 87}
{'id': '6aaMZ3fcfLv4tEbmY7bjRM', 'name': 'Isaiah Rashad', 'genres': ['hip hop', 'rap', 'tennessee hip hop', 'underground hip hop'], 'followers': {'href': None, 'total': 1422176}, 'popularity': 65}
{'id': '2IDLDx25HU1nQMKde4n61a', 'name': 'Rich Brian', 'genres': ['indonesian hip hop'], 'followers': {'href': None, 'total': 2124330}, 'popularity': 63}
{'id': '6PfSUFtkMVoDkx4MQkzOi3', 'name': '100 gecs', 'genres': ['bubblegum bass', 'hyper-rock', 'hyperpop', 'proto-hyperpop'], 'followers': {'href': None, 'total': 602862}, 'popularity': 55}
{'id': '6TVVIyd0fsRDGg6WzHKyTP', 'name': 'Durand Jones & The Indications', 'genres': ['souldies'], 'followers': {'href': None, 'total': 363579}, 'popularity': 56}
{'id': '5CMaNobmJYgXcfiT0zYOwi', 'name': 'WHIPPED CREAM', 'genres': ['canadian electronic', 'electra'], 'followers': {'href': None, 'total': 80716}, 'popularity': 46}
{'id': '6fjbZ7zQBYEy3kvB5JL5PM', 'name': 'VNSSA', 'genres': ['bass house', 'tech house'], 'followers': {'href': None, 'total': 17625}, 'popularity': 36}
{'id': '5JFFju6YhPYSjtQGtAsPaa', 'name': 'Dj LordFlockAmen', 'genres': [], 'followers': {'href': None, 'total': 1419}, 'popularity': 16}
{'id': '6xfqnpe2HnLVUaYXs2F8YS', 'name': 'Anna Kendrick', 'genres': ['hollywood', 'movie tunes'], 'followers': {'href': None, 'total': 354628}, 'popularity': 64}
{'id': '6vdIozDDsH4uJEAHN8tyS3', 'name': 'Paco Osuna', 'genres': ['minimal techno', 'spanish techno', 'techno'], 'followers': {'href': None, 'total': 90665}, 'popularity': 31}
{'id': '52qyXsgOeHZZxHRRxyO8xm', 'name': 'Sama’ Abdulhadi', 'genres': ['arab electronic', 'palestinian alternative'], 'followers': {'href': None, 'total': 46234}, 'popularity': 21}
{'id': '4PRhpvNBYt7YURb5VhEDox', 'name': 'DJ Holographic', 'genres': [], 'followers': {'href': None, 'total': 4240}, 'popularity': 16}
{'id': '3StMSo3rzsZBfuGaGLq3IE', 'name': 'Layla Benitez', 'genres': [], 'followers': {'href': None, 'total': 5634}, 'popularity': 25}
{'id': '6bprXdW2g8kg49tNslPQ6X', 'name': 'Miane', 'genres': ['tech house'], 'followers': {'href': None, 'total': 9674}, 'popularity': 30}
{'id': '3qiHUAX7zY4Qnjx8TNUzVx', 'name': 'Yeat', 'genres': ['pluggnb', 'rage rap'], 'followers': {'href': None, 'total': 4710684}, 'popularity': 80}
{'id': '5cj0lLjcoR7YOSnhnX0Po5', 'name': 'Doja Cat', 'genres': ['dance pop', 'pop'], 'followers': {'href': None, 'total': 29657941}, 'popularity': 84}
{'id': '790FomKkXshlbRYZFtlgla', 'name': 'KAROL G', 'genres': ['reggaeton', 'reggaeton colombiano', 'trap latino', 'urbano latino'], 'followers': {'href': None, 'total': 45892715}, 'popularity': 86}
{'id': '2C6i0I5RiGzDKN9IAF8reh', 'name': 'Banda MS de Sergio Lizárraga', 'genres': ['banda', 'musica mexicana', 'norteno'], 'followers': {'href': None, 'total': 17182213}, 'popularity': 76}
{'id': '3MZsBdqDrRTJihTHQrO6Dq', 'name': 'Joji', 'genres': ['viral pop'], 'followers': {'href': None, 'total': 9462104}, 'popularity': 75}
{'id': '37M5pPGs6V1fchFJSgCguX', 'name': 'FINNEAS', 'genres': ['alt z', 'la indie', 'pop', 'pov: indie'], 'followers': {'href': None, 'total': 1939772}, 'popularity': 63}
{'id': '5IH6FPUwQTxPSXurCrcIov', 'name': 'Alec Benjamin', 'genres': ['alt z', 'pop', 'pov: indie'], 'followers': {'href': None, 'total': 5010119}, 'popularity': 72}
{'id': '47opbYEKDjXnRk9uLscp11', 'name': 'Yolanda Adams', 'genres': ['gospel', 'gospel r&b'], 'followers': {'href': None, 'total': 752496}, 'popularity': 46}
{'id': '7HO5fOXE4gh3lzZn64tX2E', 'name': 'Mariah the Scientist', 'genres': ['alternative r&b'], 'followers': {'href': None, 'total': 397986}, 'popularity': 62}
{'id': '3VHAySZQPlfGlNLslzXYpN', 'name': 'NATHY PELUSO', 'genres': ['r&b en espanol', 'rap latina'], 'followers': {'href': None, 'total': 1147628}, 'popularity': 61}
{'id': '3XxNRirzbjfLdDli06zMaB', 'name': 'Eyedress', 'genres': ['noise pop', 'pov: indie'], 'followers': {'href': None, 'total': 1880403}, 'popularity': 72}
{'id': '4kSGbjWGxTchKpIxXPJv0B', 'name': 'Crumb', 'genres': ['neo-psychedelic'], 'followers': {'href': None, 'total': 544430}, 'popularity': 56}
{'id': '2nAKP6etu8wXNnezKXgqgg', 'name': 'Viagra Boys', 'genres': ['crank wave', 'stockholm indie'], 'followers': {'href': None, 'total': 293199}, 'popularity': 50}
{'id': '3SGLeWc7J5Ve0CinAOrb3a', 'name': 'Skegss', 'genres': ['australian surf rock', 'indie surf'], 'followers': {'href': None, 'total': 230704}, 'popularity': 46}
{'id': '37PL04N8kBWWW69jdrMMWT', 'name': 'Altin Gün', 'genres': ['dutch indie', 'turkish psych'], 'followers': {'href': None, 'total': 332273}, 'popularity': 48}
{'id': '7fWD0BSDlixbj6YNQyHTLh', 'name': 'Cariño', 'genres': ['tontipop'], 'followers': {'href': None, 'total': 101704}, 'popularity': 45}
{'id': '0elWFr7TW8piilVRYJUe4P', 'name': 'Natanael Cano', 'genres': ['corrido', 'corridos tumbados', 'musica mexicana', 'sad sierreno', 'sierreno'], 'followers': {'href': None, 'total': 12337396}, 'popularity': 86}
{'id': '4rTv3Ejc7hKMtmoBOK1B4T', 'name': 'Ali Gatie', 'genres': ['canadian hip hop'], 'followers': {'href': None, 'total': 2067571}, 'popularity': 62}
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{'id': '3NegWDGp038A3FIi3gSYzl', 'name': 'Ali Sethi', 'genres': ['classic pakistani pop', 'indian indie', 'pakistani pop', 'sufi'], 'followers': {'href': None, 'total': 398755}, 'popularity': 53}
{'id': '3rWZHrfrsPBxVy692yAIxF', 'name': 'WILLOW', 'genres': ['afrofuturism', 'pop', 'post-teen pop', 'pov: indie'], 'followers': {'href': None, 'total': 2523754}, 'popularity': 67}
{'id': '3Uqu1mEdkUJxPe7s31n1M9', 'name': 'Weyes Blood', 'genres': ['art pop', 'chamber pop', 'dream pop', 'experimental folk', 'experimental pop', 'indie pop'], 'followers': {'href': None, 'total': 452786}, 'popularity': 55}
{'id': '6aiFCgyKNwF9Rv5TOxnE8E', 'name': 'IDK', 'genres': ['pop rap', 'underground hip hop'], 'followers': {'href': None, 'total': 473537}, 'popularity': 54}
{'id': '1SCrMreNPJYSRZIlRe9SUq', 'name': 'Paris Texas', 'genres': ['chill abstract hip hop', 'indie hip hop'], 'followers': {'href': None, 'total': 137844}, 'popularity': 47}
{'id': '6caPJFLv1wesmM7gwK1ACy', 'name': 'Boris Brejcha', 'genres': ['german house', 'german techno', 'high-tech minimal', 'minimal techno'], 'followers': {'href': None, 'total': 1359368}, 'popularity': 59}
{'id': '31W5EY0aAly4Qieq6OFu6I', 'name': 'A Boogie Wit da Hoodie', 'genres': ['melodic rap', 'rap', 'trap'], 'followers': {'href': None, 'total': 8122882}, 'popularity': 77}
{'id': '1kfWoWgCugPkyxQP8lkRlY', 'name': 'Jackson Wang', 'genres': ['k-pop'], 'followers': {'href': None, 'total': 4179612}, 'popularity': 59}
{'id': '7tm9Tuc70geXOOyKhtZHIj', 'name': 'Chris Lorenzo', 'genres': ['bass house', 'house', 'tech house'], 'followers': {'href': None, 'total': 115094}, 'popularity': 56}
{'id': '3MdXrJWsbVzdn6fe5JYkSQ', 'name': 'Latto', 'genres': ['trap queen'], 'followers': {'href': None, 'total': 1577835}, 'popularity': 74}
{'id': '3x3jSlhyv5CiCZDZlaTq0M', 'name': 'Pi’erre Bourne', 'genres': ['melodic rap'], 'followers': {'href': None, 'total': 1007188}, 'popularity': 58}
{'id': '6s3XaJkcT7464G4oII9V41', 'name': 'Caleb Gordon', 'genres': [], 'followers': {'href': None, 'total': 195384}, 'popularity': 59}
{'id': '1btv9qmIpbp7q1ixCYNdHu', 'name': 'Adam Beyer', 'genres': ['dark techno', 'minimal techno', 'raw techno', 'schranz', 'swedish electronic', 'swedish techno', 'techno'], 'followers': {'href': None, 'total': 317976}, 'popularity': 50}
{'id': '1ChtRJ3f4rbv4vtz87i6CD', 'name': 'Cassian', 'genres': ['melodic house'], 'followers': {'href': None, 'total': 53607}, 'popularity': 50}
{'id': '2kLa7JZu4Ijdz1Gle2khZh', 'name': 'TSHA', 'genres': ['uk house'], 'followers': {'href': None, 'total': 70743}, 'popularity': 46}
{'id': '57sPl5iWgq5t6AscVbMTOW', 'name': 'Airrica', 'genres': [], 'followers': {'href': None, 'total': 853}, 'popularity': 10}
{'id': '2QKPp6hTeYMCSemGtfAmXx', 'name': 'Minus the Light', 'genres': [], 'followers': {'href': None, 'total': 191}, 'popularity': 1}
In [ ]:
# get the top tracks for each artist in the dataset
unique_artists = set(artist_dataset["id"].to_list())
song_dataset = pd.DataFrame(columns=['id', 'name', 'artists', 'duration', 'explicit', 'popularity'])

out = "data/song_dataset_2.csv"

starting_id = "16QCJENzcdhwka9bTKYMVB"
starting = False
for a_id in unique_artists:
    if a_id == starting_id:
        starting = True
        continue
    
    if not starting:
        print("skipped")
        continue

    try:
        t = get_top_tracks(a_id, auth)
        new_songs = pd.DataFrame(extract_top_tracks(t))
        print(f"{len(new_songs)} songs ADDED for artist: {a_id}")
        song_dataset = pd.concat([song_dataset, new_songs], ignore_index=True)
    except LookupError as e:
        print(f"No songs found for artist: {a_id}, adding to list.")
        remove_artist(a_id)
    except BaseException as et:
        print(et)
        song_dataset.to_csv(out)
        break

song_dataset.to_csv(out)

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10 songs ADDED for artist: 3TQKbfmV71guAH49oalx69
10 songs ADDED for artist: 6vXYoy8ouRVib302zxaxFF
10 songs ADDED for artist: 0X2BH1fck6amBIoJhDVmmJ
10 songs ADDED for artist: 7hDLm8O99kRfpY3DjFHQAP
8 songs ADDED for artist: 0qdYyb3F4y3i3J4zYrA21k
10 songs ADDED for artist: 17lzZA2AlOHwCwFALHttmp
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In [ ]:
coachella_perf = pd.read_csv("data/coachella_artists.csv")
coachella_artists = coachella_perf.drop_duplicates(subset='id')
id_list = coachella_artists['name'].to_list()
genres = pd.DataFrame(columns=['name', 'genres'])
In [ ]:
coachella_artist_meta = []
for id in id_list:
    a_json = get_artist(id, auth)
    row = extract_artist(a_json)
    print(row)
    coachella_artist_meta.append(row)

{'id': '181bsRPaVXVlUKXrxwZfHK', 'name': 'Megan Thee Stallion', 'genres': ['houston rap', 'pop', 'r&b', 'rap', 'trap queen'], 'followers': {'href': None, 'total': 8958891}, 'popularity': 75}
{'id': '1AhjOkOLkbHUfcHDSErXQs', 'name': '88rising', 'genres': ['asian american hip hop'], 'followers': {'href': None, 'total': 972339}, 'popularity': 60}
{'id': '4fxd5Ee7UefO4CUXgwJ7IP', 'name': 'Giveon', 'genres': ['r&b'], 'followers': {'href': None, 'total': 3731383}, 'popularity': 73}
{'id': '4Uc8Dsxct0oMqx0P6i60ea', 'name': 'Conan Gray', 'genres': ['bedroom pop', 'pop', 'pov: indie'], 'followers': {'href': None, 'total': 9552638}, 'popularity': 76}
{'id': '3ycxRkcZ67ALN3GQJ57Vig', 'name': 'Masego', 'genres': ['alternative r&b', 'indie soul'], 'followers': {'href': None, 'total': 1360195}, 'popularity': 69}
{'id': '1gWjcmBsveEYMxOZ0VRi32', 'name': 'Koffee', 'genres': ['dancehall', 'dancehall queen', 'jamaican dancehall', 'modern reggae'], 'followers': {'href': None, 'total': 716570}, 'popularity': 53}
{'id': '5qBZETtyzfYnXOobDXbmcD', 'name': 'Danny Elfman', 'genres': ['orchestral soundtrack', 'soundtrack'], 'followers': {'href': None, 'total': 405374}, 'popularity': 57}
{'id': '2vnB6tuQMaQpORiRdvXF9H', 'name': 'Beach Bunny', 'genres': ['bubblegrunge', 'chicago indie', 'indie pop', 'pov: indie'], 'followers': {'href': None, 'total': 1404257}, 'popularity': 66}
{'id': '5JMLG56F1X5mFmWNmS0iAp', 'name': 'Chelsea Cutler', 'genres': ['alt z'], 'followers': {'href': None, 'total': 710004}, 'popularity': 62}
{'id': '1nVq0hKIVReeaiB3xJgKf0', 'name': 'Molchat Doma', 'genres': ['belarusian indie', 'russian post-punk', 'sovietwave'], 'followers': {'href': None, 'total': 1233440}, 'popularity': 59}
{'id': '7Hvq85OU8T7Hsd63zNBwaL', 'name': 'black midi', 'genres': ['avant-prog', 'brutal prog', 'crank wave', 'noise rock', 'uk noise rock', 'uk post-punk revival'], 'followers': {'href': None, 'total': 256869}, 'popularity': 41}
{'id': '6AQEfqGPSxZX0nJVonYxi6', 'name': 'Inner Wave', 'genres': ['indie garage rock'], 'followers': {'href': None, 'total': 207317}, 'popularity': 46}
{'id': '33yje3hgpNfdXpqdJQcrt9', 'name': 'Mannequin Pussy', 'genres': ['bubblegrunge', 'indie punk', 'modern hardcore', 'modern power pop', 'riot grrrl'], 'followers': {'href': None, 'total': 155112}, 'popularity': 50}
{'id': '2UZIAOlrnyZmyzt1nuXr9y', 'name': 'Nicki Nicole', 'genres': ['argentine hip hop', 'r&b argentino', 'trap argentino', 'trap latino', 'urbano latino'], 'followers': {'href': None, 'total': 6556429}, 'popularity': 76}
{'id': '3JSSjGYcIkgsrz7892CelT', 'name': 'Ed Maverick', 'genres': ['chihuahua indie', 'indie triste', 'mexican indie'], 'followers': {'href': None, 'total': 3190799}, 'popularity': 65}
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{'id': '13dTrWNNrnZ3AkgNyQNKP5', 'name': 'The Linda Lindas', 'genres': ['riot grrrl'], 'followers': {'href': None, 'total': 139984}, 'popularity': 36}
{'id': '0avMDS4HyoCEP6RqZJWpY2', 'name': 'Ethel Cain', 'genres': ['countrygaze', 'indie pop', 'modern dream pop'], 'followers': {'href': None, 'total': 523257}, 'popularity': 60}
{'id': '7KeN0XX71T4fGysIYLB5J5', 'name': 'Destroy Boys', 'genres': ['oakland indie', 'pov: indie'], 'followers': {'href': None, 'total': 443790}, 'popularity': 55}
{'id': '0UTzLuwz9RvFOCnwAZjUxn', 'name': 'Bratty', 'genres': ['sinaloa indie'], 'followers': {'href': None, 'total': 375756}, 'popularity': 54}
{'id': '6hW33nVHPElaXuG2hQ0QOp', 'name': 'Scowl', 'genres': ['california hardcore', 'modern hardcore'], 'followers': {'href': None, 'total': 64644}, 'popularity': 36}
{'id': '2FDvUb4YgyUPpmnm1ILPra', 'name': 'Horsegirl', 'genres': ['american post-punk', 'crank wave'], 'followers': {'href': None, 'total': 42957}, 'popularity': 26}
{'id': '2I4hRNCYkPKJQlkoEZKjYx', 'name': 'Monolink', 'genres': ['livetronica'], 'followers': {'href': None, 'total': 478680}, 'popularity': 56}
{'id': '5XJDexmWFLWOkjOEjOVX3e', 'name': 'Eladio Carrion', 'genres': ['trap boricua', 'trap latino', 'trap triste', 'urbano latino'], 'followers': {'href': None, 'total': 6649883}, 'popularity': 79}
{'id': '1OFOShsIbhy1l5x73yuVyB', 'name': 'Shenseea', 'genres': ['dancehall', 'dancehall queen'], 'followers': {'href': None, 'total': 726955}, 'popularity': 61}
{'id': '5SHgclK1ZpTdfdAmXW7J6s', 'name': 'The Last Dinner Party', 'genres': [], 'followers': {'href': None, 'total': 362660}, 'popularity': 64}
{'id': '4ClziihVpBeFXNyDH83Lde', 'name': 'UMI', 'genres': ['alternative r&b', 'bedroom soul'], 'followers': {'href': None, 'total': 1012224}, 'popularity': 61}
{'id': '0jIWKlfmD4Ew7HeVVrq03g', 'name': 'Elyanna', 'genres': ['palestinian pop'], 'followers': {'href': None, 'total': 145835}, 'popularity': 53}
{'id': '7LazT0w9pUsf2yHwhWskct', 'name': 'Dxsko', 'genres': [], 'followers': {'href': None, 'total': 2}, 'popularity': 0}
{'id': '2feDdbD5araYcm6JhFHHw7', 'name': 'Labrinth', 'genres': ['indie poptimism', 'pop'], 'followers': {'href': None, 'total': 3215340}, 'popularity': 75}
{'id': '53wZLCnhshZKDRs3uopFBc', 'name': 'Jai Paul', 'genres': ['escape room', 'experimental r&b'], 'followers': {'href': None, 'total': 206631}, 'popularity': 42}
{'id': '0NB5HROxc8dDBXpkIi1v3d', 'name': 'Remi Wolf', 'genres': ['indie pop', 'modern alternative pop'], 'followers': {'href': None, 'total': 466866}, 'popularity': 62}
{'id': '67lytN32YpUxiSeWlKfHJ3', 'name': 'Yung Lean', 'genres': ['cloud rap', 'psychedelic hip hop', 'underground hip hop'], 'followers': {'href': None, 'total': 920172}, 'popularity': 63}
{'id': '22KyrgRdE2K6aB5wtZls3c', 'name': 'AG Club', 'genres': ['indie hip hop'], 'followers': {'href': None, 'total': 118427}, 'popularity': 47}
{'id': '7xqDhJt8ashQEKlRnWiKFt', 'name': 'Wave Groove', 'genres': [], 'followers': {'href': None, 'total': 224}, 'popularity': 0}
{'id': '1VPmR4DJC1PlOtd0IADAO0', 'name': '$uicideboy$', 'genres': ['cloud rap', 'dark trap', 'new orleans rap', 'underground hip hop'], 'followers': {'href': None, 'total': 7032393}, 'popularity': 83}
{'id': '2tIP7SsRs7vjIcLrU85W8J', 'name': 'The Kid LAROI', 'genres': ['australian hip hop'], 'followers': {'href': None, 'total': 5250981}, 'popularity': 75}
{'id': '2FKWNmZWDBZR4dE5KX4plR', 'name': 'Diljit Dosanjh', 'genres': ['filmi', 'modern bollywood', 'punjabi pop'], 'followers': {'href': None, 'total': 19910179}, 'popularity': 75}
{'id': '2vf4pRsEY6LpL5tKmqWb64', 'name': 'Elderbrook', 'genres': ['uk dance'], 'followers': {'href': None, 'total': 421790}, 'popularity': 64}
{'id': '1rHOtdmGNr5vcYNw5v7QGC', 'name': 'Kenny Beats', 'genres': ['indie hip hop', 'trap', 'underground hip hop'], 'followers': {'href': None, 'total': 202674}, 'popularity': 57}
{'id': '08PvCOlef4xdOr20jFSTPd', 'name': 'Flo Milli', 'genres': ['viral rap'], 'followers': {'href': None, 'total': 1051595}, 'popularity': 71}
{'id': '26WKgv73kRHD0gEDKD1i8j', 'name': 'Keinemusik', 'genres': ['melodic house'], 'followers': {'href': None, 'total': 152601}, 'popularity': 57}
{'id': '50Lr1puweM1hFsF1LpIZLM', 'name': 'WhoMadeWho', 'genres': ['alternative dance', 'dance-punk', 'danish indie', 'electronica', 'experimental pop'], 'followers': {'href': None, 'total': 232419}, 'popularity': 53}
{'id': '6QSwQEz8CDMg8Rqk8dEkxS', 'name': 'Mathame', 'genres': ['italian techno'], 'followers': {'href': None, 'total': 146057}, 'popularity': 49}
{'id': '5vr1GvUstxr6nwxniKqLOh', 'name': 'Colyn', 'genres': ['melodic techno'], 'followers': {'href': None, 'total': 76215}, 'popularity': 46}
{'id': '68ywCN6ZpInbcilOfLBa3a', 'name': 'Chloé Caillet', 'genres': [], 'followers': {'href': None, 'total': 14284}, 'popularity': 47}
{'id': '44qAhQu52dYKcHOFQd3esf', 'name': 'Francis Mercier', 'genres': ['melodic house'], 'followers': {'href': None, 'total': 43902}, 'popularity': 56}
{'id': '1QPWPWRTE4DWlWmU0bFzzS', 'name': 'Talon Proc Alford', 'genres': [], 'followers': {'href': None, 'total': 27}, 'popularity': 21}
{'id': '2qoQgPAilErOKCwE2Y8wOG', 'name': 'GloRilla', 'genres': ['southern hip hop'], 'followers': {'href': None, 'total': 676760}, 'popularity': 70}
{'id': '2FS22haX3FYbyOsUAkuYqZ', 'name': 'Los Fabulosos Cadillacs', 'genres': ['argentine rock', 'latin alternative', 'latin pop', 'latin rock', 'rock en espanol', 'ska', 'ska argentino'], 'followers': {'href': None, 'total': 3029479}, 'popularity': 64}
{'id': '6USv9qhCn6zfxlBQIYJ9qs', 'name': 'Dominic Fike', 'genres': ['alternative pop rock', 'pov: indie'], 'followers': {'href': None, 'total': 1652402}, 'popularity': 74}
{'id': '0PxzGnCYBpSuaI49OR94cA', 'name': 'Big Wild', 'genres': ['electropop', 'vapor soul'], 'followers': {'href': None, 'total': 268655}, 'popularity': 55}
{'id': '5SXEylV07TC57eanSxxg4R', 'name': 'Stick Figure', 'genres': ['reggae fusion', 'reggae rock'], 'followers': {'href': None, 'total': 666174}, 'popularity': 65}
{'id': '4qrHkx5cgWIslciLXUMrYw', 'name': 'Knocked Loose', 'genres': ['kentucky metal', 'kentucky punk'], 'followers': {'href': None, 'total': 412436}, 'popularity': 60}
{'id': '7riQPkkGZBnTh9ve5qIhYo', 'name': 'Mareux', 'genres': ['dark synthpop'], 'followers': {'href': None, 'total': 382889}, 'popularity': 61}
{'id': '6lcwlkAjBPSKnFBZjjZFJs', 'name': 'Alex G', 'genres': ['philly indie', 'pov: indie'], 'followers': {'href': None, 'total': 1914593}, 'popularity': 72}
{'id': '5Wj0an60VgRckYV9zlDe1e', 'name': 'Momma', 'genres': ['bubblegrunge', 'indie rock', 'small room'], 'followers': {'href': None, 'total': 61063}, 'popularity': 41}
{'id': '0otAqZw8htTsGHfqR491Yh', 'name': 'Sleaford Mods', 'genres': ['crank wave', 'nottingham hip hop', 'nottingham indie'], 'followers': {'href': None, 'total': 238324}, 'popularity': 42}
{'id': '0cLrgpG3pW4MX4nD8h6qCc', 'name': 'El Michels Affair', 'genres': ['afrobeat', 'funk', 'instrumental soul'], 'followers': {'href': None, 'total': 181786}, 'popularity': 49}
{'id': '07b9qW7pabKGO29JPWXn9m', 'name': 'Los Bitchos', 'genres': [], 'followers': {'href': None, 'total': 89553}, 'popularity': 37}
{'id': '4VNRWgZyB5AiSw4jlGDVLy', 'name': 'Conexión Divina', 'genres': ['sad sierreno'], 'followers': {'href': None, 'total': 12277}, 'popularity': 32}
{'id': '2IW1E4rZStaUEm8xlpuqUe', 'name': 'Argenis Carruyo', 'genres': ['classic venezuelan pop'], 'followers': {'href': None, 'total': 78039}, 'popularity': 31}
{'id': '7FtCyCJCJaxabYO7Uyda5B', 'name': 'Cannons', 'genres': ['chillwave'], 'followers': {'href': None, 'total': 353639}, 'popularity': 58}
{'id': '3X2DdnmoANw8Rg8luHyZQb', 'name': 'Romy', 'genres': [], 'followers': {'href': None, 'total': 107778}, 'popularity': 55}
{'id': '6trIghKwHRUyxwvm66HLHH', 'name': 'Fousheé', 'genres': ['indie r&b'], 'followers': {'href': None, 'total': 202422}, 'popularity': 57}
{'id': '5XMyhVhi5ZN2pi0Qwi1zXS', 'name': 'Joy Crookes', 'genres': ['alternative r&b', 'uk contemporary r&b'], 'followers': {'href': None, 'total': 437551}, 'popularity': 55}
{'id': '3NegWDGp038A3FIi3gSYzl', 'name': 'Ali Sethi', 'genres': ['classic pakistani pop', 'indian indie', 'pakistani pop', 'sufi'], 'followers': {'href': None, 'total': 398755}, 'popularity': 53}
{'id': '3rWZHrfrsPBxVy692yAIxF', 'name': 'WILLOW', 'genres': ['afrofuturism', 'pop', 'post-teen pop', 'pov: indie'], 'followers': {'href': None, 'total': 2523754}, 'popularity': 67}
{'id': '3Uqu1mEdkUJxPe7s31n1M9', 'name': 'Weyes Blood', 'genres': ['art pop', 'chamber pop', 'dream pop', 'experimental folk', 'experimental pop', 'indie pop'], 'followers': {'href': None, 'total': 452786}, 'popularity': 55}
{'id': '6aiFCgyKNwF9Rv5TOxnE8E', 'name': 'IDK', 'genres': ['pop rap', 'underground hip hop'], 'followers': {'href': None, 'total': 473537}, 'popularity': 54}
{'id': '1SCrMreNPJYSRZIlRe9SUq', 'name': 'Paris Texas', 'genres': ['chill abstract hip hop', 'indie hip hop'], 'followers': {'href': None, 'total': 137844}, 'popularity': 47}
{'id': '6caPJFLv1wesmM7gwK1ACy', 'name': 'Boris Brejcha', 'genres': ['german house', 'german techno', 'high-tech minimal', 'minimal techno'], 'followers': {'href': None, 'total': 1359368}, 'popularity': 59}
{'id': '31W5EY0aAly4Qieq6OFu6I', 'name': 'A Boogie Wit da Hoodie', 'genres': ['melodic rap', 'rap', 'trap'], 'followers': {'href': None, 'total': 8122882}, 'popularity': 77}
{'id': '1kfWoWgCugPkyxQP8lkRlY', 'name': 'Jackson Wang', 'genres': ['k-pop'], 'followers': {'href': None, 'total': 4179612}, 'popularity': 59}
{'id': '7tm9Tuc70geXOOyKhtZHIj', 'name': 'Chris Lorenzo', 'genres': ['bass house', 'house', 'tech house'], 'followers': {'href': None, 'total': 115094}, 'popularity': 56}
{'id': '3MdXrJWsbVzdn6fe5JYkSQ', 'name': 'Latto', 'genres': ['trap queen'], 'followers': {'href': None, 'total': 1577835}, 'popularity': 74}
{'id': '3x3jSlhyv5CiCZDZlaTq0M', 'name': 'Pi’erre Bourne', 'genres': ['melodic rap'], 'followers': {'href': None, 'total': 1007188}, 'popularity': 58}
{'id': '6s3XaJkcT7464G4oII9V41', 'name': 'Caleb Gordon', 'genres': [], 'followers': {'href': None, 'total': 195384}, 'popularity': 59}
{'id': '1btv9qmIpbp7q1ixCYNdHu', 'name': 'Adam Beyer', 'genres': ['dark techno', 'minimal techno', 'raw techno', 'schranz', 'swedish electronic', 'swedish techno', 'techno'], 'followers': {'href': None, 'total': 317976}, 'popularity': 50}
{'id': '1ChtRJ3f4rbv4vtz87i6CD', 'name': 'Cassian', 'genres': ['melodic house'], 'followers': {'href': None, 'total': 53607}, 'popularity': 50}
{'id': '2kLa7JZu4Ijdz1Gle2khZh', 'name': 'TSHA', 'genres': ['uk house'], 'followers': {'href': None, 'total': 70743}, 'popularity': 46}
{'id': '57sPl5iWgq5t6AscVbMTOW', 'name': 'Airrica', 'genres': [], 'followers': {'href': None, 'total': 853}, 'popularity': 10}
{'id': '2QKPp6hTeYMCSemGtfAmXx', 'name': 'Minus the Light', 'genres': [], 'followers': {'href': None, 'total': 191}, 'popularity': 1}

Song and Lyric Data Collection¶

This section queries the MusixMatch API to search for the top 5 songs that have lyrics for each artist that performed on the Main Stage. The list of artists is taken from the Artists dataset that we previously made. Once the songs have been found, we query the API again to get the lyric data.

In [ ]:
mm_access_token = ""

# Collect the artists that performed on the main stage at Coachella
coachella_perf = pd.read_csv("data/coachella_artists.csv")
coachella_artists = coachella_perf.drop_duplicates(subset='id')
name_list = coachella_artists['name'].to_list()

main_stage = coachella_perf[coachella_perf['stage'] == "Coachella Stage"].drop_duplicates('id')['name'].to_list()
main_stage

['Beck',
 'The Chemical Brothers',
 'Morrissey',
 'Modest Mouse',
 'Tool',
 'Rage Against the Machine',
 'Pavement',
 'Thievery Corporation',
 'Mix Master Mike',
 'Paul Oakenfold',
 'Weezer',
 'The Roots',
 'MC Supernatural',
 'Iggy Pop',
 'The Dandy Warhols',
 'Souls of Mischief',
 'Medusa',
 'Ozomatli',
 'Sigur Rós',
 'Björk',
 'Siouxsie & the Banshees',
 'Jack Johnson',
 'Pete Yorn',
 'G. Love & Special Sauce',
 'KRS-One',
 'Princess Superstar',
 'Vin Rock',
 'Queens of the Stone Age',
 'Saul Williams',
 'The  Noise Conspiracy',
 'Oasis',
 'The Prodigy',
 'Foo Fighters',
 'The Strokes',
 'Saves the Day',
 'Belle & Sebastian',
 'The Mars Volta',
 'Tiësto',
 'Beastie Boys',
 'Ben Harper & the Innocent Criminals',
 'Blur',
 'The Hives',
 'The Donnas',
 'The Mooney Suzuki',
 'Kinky',
 'Red Hot Chili Peppers',
 'Iggy and the Stooges',
 'The White Stripes',
 'Sonic Youth',
 'The Polyphonic Spree',
 'The Soundtrack of Our Lives',
 'Whirlwind Heat',
 'Interpol',
 'Café Tacvba',
 'Radiohead',
 'Pixies',
 'Sparta',
 'Hieroglyphics',
 'The Sounds',
 'The Section Quartet',
 'Death Cab for Cutie',
 'Kraftwerk',
 'Moving Units',
 'The Black Keys',
 'LCD Soundsystem',
 'The Cure',
 'The Flaming Lips',
 'Air',
 'Thursday',
 'Muse',
 'Antibalas Afrobeat Orchestra',
 'Thelonious Monster',
 'Bright Eyes',
 'Broken Social Scene',
 'The Killers',
 'Coldplay',
 'Bauhaus',
 'Wilco',
 'Keane',
 'Snow Patrol',
 'The Raveonettes',
 'Buck 65',
 'Spoon',
 'M83',
 'Black Star',
 'Nine Inch Nails',
 'New Order',
 'Gang of Four',
 'The Futureheads',
 'Thrice',
 'The Perceptionists',
 'Gram Rabbit',
 'Arcade Fire',
 'Tegan and Sara',
 'Shout Out Louds',
 'M.I.A.',
 'Depeche Mode',
 'Franz Ferdinand',
 'Kanye West',
 'Common',
 'The Duke Spirit',
 'The Walkmen',
 'The New Amsterdams',
 'My Morning Jacket',
 'Animal Collective',
 'TV on the Radio',
 'Brother Ali',
 'Massive Attack',
 'Yeah Yeah Yeahs',
 'Sleater-Kinney',
 'Matisyahu',
 'The Magic Numbers',
 'Los Amigos Invisibles',
 'Youth Group',
 'Minus the Bear',
 'Kaskade',
 'Coheed and Cambria',
 'Metric',
 'Phoenix',
 'The Jesus and Mary Chain',
 'Arctic Monkeys',
 'Silversun Pickups',
 'Satellite Party',
 'Flosstradamus',
 'Gogol Bordello',
 'Circa Survive',
 'Tokyo Police Club',
 'Kings of Leon',
 'Travis',
 'Regina Spektor',
 'Fountains of Wayne',
 'Pharoahe Monch',
 "Jack's Mannequin",
 'Justice',
 'Peter Bjorn and John',
 'Hot Chip',
 'Girl Talk',
 'Crowded House',
 'Willie Nelson',
 'Explosions in the Sky',
 'The Feeling',
 'Lupe Fiasco',
 'Mika',
 'Kaiser Chiefs',
 'The Verve',
 'The Raconteurs',
 'The Breeders',
 'Slightly Stoopid',
 'John Butler Trio',
 'Rogue Wave',
 'Vampire Weekend',
 'Prince',
 'Portishead',
 'Cold War Kids',
 'VHS or Beta',
 'MGMT',
 'Calvin Harris',
 'Roger Waters',
 'Sean Penn',
 'Stars',
 'The Cool Kids',
 'Austin TV',
 'Chromeo',
 'Sia',
 'Paul McCartney',
 'The Airborne Toxic Event',
 'We Are Scientists',
 'Cage the Elephant',
 'Michael Franti & Spearhead',
 'Paolo Nutini',
 'Joss Stone',
 'Billy Talent',
 'My Bloody Valentine',
 'Okkervil River',
 'The Knux',
 'The Gaslight Anthem',
 'Jay-Z',
 'Them Crooked Vultures',
 'The Specials',
 'Street Sweeper Social Club',
 'Calle 13',
 'Wale',
 'Passion Pit',
 'Faith No More',
 'White Rabbits',
 'Old Crow Medicine Show',
 'Zoé',
 'Steel Train',
 'The xx',
 'Band of Skulls',
 'Portugal. The Man',
 'Major Lazer',
 'Gorillaz',
 'Yo La Tengo',
 'De La Soul',
 'B.o.B',
 'Babasonicos',
 'Local Natives',
 'Ms. Lauryn Hill',
 'Cee Lo Green',
 'Tame Impala',
 'Marina and the Diamonds',
 'Alf Alpha',
 'Gabe Real',
 'Mumford & Sons',
 'Erykah Badu',
 'Bomba Estéreo',
 'Trampled by Turtles',
 'Two Door Cinema Club',
 'Foals',
 'Duran Duran',
 'Death from Above 1979',
 'Nas & Damian Marley',
 'Wiz Khalifa',
 'Los Bunkers',
 'City and Colour',
 'Foster the People',
 'Ellie Goulding',
 'Swedish House Mafia',
 'Pulp',
 'James',
 'Kendrick Lamar',
 'Hello Seahorse!',
 'GabeReal',
 'Frank Ocean',
 'Gary Clark, Jr.',
 'Grouplove',
 'Bon Iver',
 'The Shins',
 "Noel Gallagher's High Flying Birds",
 'Awolnation',
 'Childish Gambino',
 'We Are Augustines',
 'Juice',
 'Azealia Banks',
 'The Head and the Heart',
 'At the Drive-In',
 'Fitz and the Tantrums',
 'Santigold',
 'Florence and the Machine',
 'The Weeknd',
 'Porter Robinson',
 'Zedd',
 'Jimbo Jenkins',
 'The Stone Roses',
 'Dâm-Funk',
 'Tokyo Ska Paradise Orchestra',
 'Skinny Lister',
 'Of Monsters and Men',
 'The Neighbourhood',
 'The Postal Service',
 'Violent Femmes',
 'Dropkick Murphys',
 'Biffy Clyro',
 'Theophilus London',
 'Vintage Trouble',
 'DJ Gabe Real',
 'Action Bronson',
 'Janelle Monáe',
 'Pusha T',
 'Nick Cave and the Bad Seeds',
 'Social Distortion',
 'The Lumineers',
 'Ghost B.C.',
 'Slipping Into Darkness',
 'Disclosure',
 'Father John Misty',
 'OutKast',
 'AFI',
 'MS MR',
 'Anthony Green',
 'The Preatures',
 'Haim',
 'Flume',
 'Bonobo',
 'Kid Cudi',
 'Graveyard',
 'Unlocking the Truth',
 'Lorde',
 'The Naked and Famous',
 'Chance the Rapper',
 'Fishbone',
 'The 1975',
 'Preservation Hall Jazz Band',
 'AC/DC',
 'The War on Drugs',
 'Vic Mensa',
 'Jack White',
 'alt-J',
 'Hozier',
 'Milky Chance',
 'Bad Religion',
 'Clean Bandit',
 'Glass Animals',
 'Run the Jewels',
 'Drake',
 'St. Lucia',
 'The Orwells',
 'Saint Motel',
 'MØ',
 'Vance Joy',
 'Years & Years',
 'Joey Bada$$',
 "Guns N' Roses",
 'Ice Cube',
 'CHVRCHES',
 'Moon Taxi',
 'SZA',
 'Vince Staples',
 'Matt and Kim',
 'Rancid',
 'Nathaniel Rateliff & the Night Sweats',
 'Lady Gaga',
 'Future',
 'Surf Curse',
 'Toots and the Maytals',
 'Lee Fields & The Expressions',
 'Kygo*',
 'SuperDuperKyle',
 'Los Ángeles Azules',
 'Daniel Caesar',
 'Kali Uchis',
 'Beyoncé',
 'Tyler, the Creator',
 'Wizkid',
 'Eminem',
 'Odesza',
 'Cardi B',
 'LANY',
 'Lion Babe',
 'Princess Nokia',
 'Kacey Musgraves',
 'Mon Laferte',
 'Los Tucanes de Tijuana',
 'Blackpink',
 'J Balvin',
 'Bazzi',
 'Sabrina Claudio',
 'Mr Eazi',
 'ARIZONA',
 'Billie Eilish',
 'Maggie Rogers',
 'Ariana Grande',
 'Khalid',
 'Bad Bunny',
 'Burna Boy',
 '070 Shake',
 'Record Safari',
 'Harry Styles',
 'Lil Baby',
 'Grupo Firme',
 'Anitta',
 'Ari Lennox',
 'Megan Thee Stallion',
 '88rising',
 'Giveon',
 'Conan Gray',
 'Masego',
 'Koffee',
 'Ye',
 'Doja Cat',
 'Karol G',
 'Banda MS',
 'Doechii',
 'Blink-182',
 'ROSALÍA',
 'Charli XCX',
 'Marc Rebillet',
 'GloRilla',
 'Los Fabulosos Cadillacs']
In [ ]:
# Search each artist from the main stage for their top 5 tracks, parse response
mm_url = f"http://api.musixmatch.com/ws/1.1/track.search"
accepted = [] # songs where the artist matches the searched artist
rejected = [] # songs where the artist does not match the searched artist
for artist in main_stage:
    search_name = artist.lower()
    # Params filters the serach to the top 5 songs by the searched artist that have lyrics data on MusixMatch (top 5 by MusixMatch's own ranking)
    params = {"apikey": mm_access_token,
              "q_artist": search_name,
              "page_size": 5,
              "page": 1,
              "f_has_lyrics": True}
    response = requests.get(mm_url, params=params)
    for track in response.json()['message']['body']['track_list']:
        # parse the response
        tdata = track['track']
        row = {"name": artist,
        "artist_name": tdata['artist_name'],
        "track_name": tdata['track_name'],
        "track_id": tdata['track_id'],
        "source": tdata}
        if track['track']['artist_name'] != artist:
            rejected.append(row)
        else:
            accepted.append(row)
In [ ]:
# Writing out the searched name and the name found for tracks where they do not match
# In the discrepancies.txt file, the rows where removed manually where the searched artist was not actually a part of the track
#   EX: Paul Oakenfold vs. Paul Oakenfold feat. Andy Gray
# This was done manually because of the amount of variation in artist names and formats for artist names from Spotify vs. MusixMatch
txt = ''
for t in rejected:
    txt += f"{t['name']}, {t['artist_name']}\n"
    
with open("data/discrepancies.txt", 'w') as f:
    f.write(txt)
In [ ]:
# Read the final version of the discrepancies.txt to move the tracks that are correct into the accepted list
lines = []
with open("data/discrepancies.txt") as f:
    lines = f.readlines()

pairs = []
for l in lines:
    x = l.split(', ')
    x[1] = x[1].replace('\n', '') 
    pairs.append((x[0], x[1]))
In [ ]:
# Move songs from the rejected list to the accepted list if they are in the discrepancies file.
count = 0
for entry in rejected:
    if (entry['name'], entry['artist_name']) in pairs:
        accepted.append(entry)
        print(entry)
        count += 1
count

{'name': 'The Chemical Brothers', 'artist_name': 'The Chemical Brothers feat. Miguel', 'track_name': 'This Is Not A Game - From "The Hunger Games: Mockingjay Part 1" Soundtrack', 'track_id': 84369062, 'source': {'track_id': 84369062, 'track_name': 'This Is Not A Game - From "The Hunger Games: Mockingjay Part 1" Soundtrack', 'track_name_translation_list': [], 'track_rating': 39, 'commontrack_id': 47417980, 'instrumental': 0, 'explicit': 0, 'has_lyrics': 1, 'has_subtitles': 1, 'has_richsync': 0, 'num_favourite': 3, 'album_id': 20900916, 'album_name': 'The Hunger Games: Mockingjay Pt. 1 (Original Motion Picture Soundtrack)', 'artist_id': 27955516, 'artist_name': 'The Chemical Brothers feat. Miguel', 'track_share_url': 'https://www.musixmatch.com/lyrics/The-Chemical-Brothers-feat-Miguel/This-Is-Not-A-Game-From-The-Hunger-Games-Mockingjay-Part-1-Soundtrack?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'track_edit_url': 'https://www.musixmatch.com/lyrics/The-Chemical-Brothers-feat-Miguel/This-Is-Not-A-Game-From-The-Hunger-Games-Mockingjay-Part-1-Soundtrack/edit?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'restricted': 0, 'updated_time': '2015-12-26T12:50:16Z', 'primary_genres': {'music_genre_list': []}}}
{'name': 'Rage Against the Machine', 'artist_name': 'Rage Against The Machine', 'track_name': 'Kick out the Jams - Live at the Grand Olympic Auditorium, Los Angeles, CA - September 2000', 'track_id': 86616280, 'source': {'track_id': 86616280, 'track_name': 'Kick out the Jams - Live at the Grand Olympic Auditorium, Los Angeles, CA - September 2000', 'track_name_translation_list': [], 'track_rating': 40, 'commontrack_id': 48741725, 'instrumental': 0, 'explicit': 1, 'has_lyrics': 1, 'has_subtitles': 1, 'has_richsync': 0, 'num_favourite': 0, 'album_id': 21049668, 'album_name': 'Live at the Grand Olympic Auditorium', 'artist_id': 220, 'artist_name': 'Rage Against The Machine', 'track_share_url': 'https://www.musixmatch.com/lyrics/Rage-Against-the-Machine/Kick-out-the-Jams-Live-Album-Version?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'track_edit_url': 'https://www.musixmatch.com/lyrics/Rage-Against-the-Machine/Kick-out-the-Jams-Live-Album-Version/edit?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'restricted': 0, 'updated_time': '2024-01-09T09:45:59Z', 'primary_genres': {'music_genre_list': [{'music_genre': {'music_genre_id': 21, 'music_genre_parent_id': 34, 'music_genre_name': 'Rock', 'music_genre_name_extended': 'Rock', 'music_genre_vanity': 'Rock'}}]}}}
{'name': 'Rage Against the Machine', 'artist_name': 'Rage Against The Machine', 'track_name': "I'm Housin' - Live at the Grand Olympic Auditorium, Los Angeles, CA - September 2000", 'track_id': 86616276, 'source': {'track_id': 86616276, 'track_name': "I'm Housin' - Live at the Grand Olympic Auditorium, Los Angeles, CA - September 2000", 'track_name_translation_list': [], 'track_rating': 40, 'commontrack_id': 48741743, 'instrumental': 0, 'explicit': 1, 'has_lyrics': 1, 'has_subtitles': 1, 'has_richsync': 0, 'num_favourite': 0, 'album_id': 21049668, 'album_name': 'Live at the Grand Olympic Auditorium', 'artist_id': 220, 'artist_name': 'Rage Against The Machine', 'track_share_url': 'https://www.musixmatch.com/lyrics/Rage-Against-the-Machine/I-m-Housin-Live-Album-Version?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'track_edit_url': 'https://www.musixmatch.com/lyrics/Rage-Against-the-Machine/I-m-Housin-Live-Album-Version/edit?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'restricted': 0, 'updated_time': '2024-01-09T09:45:57Z', 'primary_genres': {'music_genre_list': [{'music_genre': {'music_genre_id': 21, 'music_genre_parent_id': 34, 'music_genre_name': 'Rock', 'music_genre_name_extended': 'Rock', 'music_genre_vanity': 'Rock'}}]}}}
{'name': 'Rage Against the Machine', 'artist_name': 'Rage Against The Machine', 'track_name': 'Down On The Street', 'track_id': 14504698, 'source': {'track_id': 14504698, 'track_name': 'Down On The Street', 'track_name_translation_list': [{'track_name_translation': {'language': 'u0', 'translation': 'Down On The Street'}}], 'track_rating': 55, 'commontrack_id': 88934, 'instrumental': 0, 'explicit': 0, 'has_lyrics': 1, 'has_subtitles': 1, 'has_richsync': 0, 'num_favourite': 11, 'album_id': 22029752, 'album_name': 'Renegades', 'artist_id': 220, 'artist_name': 'Rage Against The Machine', 'track_share_url': 'https://www.musixmatch.com/lyrics/Rage-Against-the-Machine/Down-on-the-Street?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'track_edit_url': 'https://www.musixmatch.com/lyrics/Rage-Against-the-Machine/Down-on-the-Street/edit?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'restricted': 0, 'updated_time': '2024-01-09T09:46:03Z', 'primary_genres': {'music_genre_list': [{'music_genre': {'music_genre_id': 1144, 'music_genre_parent_id': 21, 'music_genre_name': 'Adult Alternative', 'music_genre_name_extended': 'Rock / Adult Alternative', 'music_genre_vanity': 'Rock-Adult-Alternative'}}, {'music_genre': {'music_genre_id': 20, 'music_genre_parent_id': 34, 'music_genre_name': 'Alternative', 'music_genre_name_extended': 'Alternative', 'music_genre_vanity': 'Alternative'}}]}}}
{'name': 'Rage Against the Machine', 'artist_name': 'Rage Against The Machine', 'track_name': "Zapata's Blood - Live at Pink Pop, Hilversum, Holland - May 1996", 'track_id': 241640738, 'source': {'track_id': 241640738, 'track_name': "Zapata's Blood - Live at Pink Pop, Hilversum, Holland - May 1996", 'track_name_translation_list': [], 'track_rating': 40, 'commontrack_id': 148271285, 'instrumental': 0, 'explicit': 1, 'has_lyrics': 1, 'has_subtitles': 1, 'has_richsync': 0, 'num_favourite': 0, 'album_id': 52939243, 'album_name': 'Live & Rare', 'artist_id': 220, 'artist_name': 'Rage Against The Machine', 'track_share_url': 'https://www.musixmatch.com/lyrics/Rage-Against-the-Machine/Zapata-s-Blood-Live-at-Pink-Pop-Hilversum-Holland-May-1996?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'track_edit_url': 'https://www.musixmatch.com/lyrics/Rage-Against-the-Machine/Zapata-s-Blood-Live-at-Pink-Pop-Hilversum-Holland-May-1996/edit?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'restricted': 0, 'updated_time': '2024-02-02T05:39:04Z', 'primary_genres': {'music_genre_list': [{'music_genre': {'music_genre_id': 21, 'music_genre_parent_id': 34, 'music_genre_name': 'Rock', 'music_genre_name_extended': 'Rock', 'music_genre_vanity': 'Rock'}}]}}}
{'name': 'Rage Against the Machine', 'artist_name': 'Rage Against The Machine', 'track_name': 'Bombtrack', 'track_id': 115689305, 'source': {'track_id': 115689305, 'track_name': 'Bombtrack', 'track_name_translation_list': [{'track_name_translation': {'language': 'u0', 'translation': 'Bombtrack'}}], 'track_rating': 62, 'commontrack_id': 48741734, 'instrumental': 0, 'explicit': 1, 'has_lyrics': 1, 'has_subtitles': 1, 'has_richsync': 1, 'num_favourite': 26, 'album_id': 24065187, 'album_name': 'Rage Against The Machine - XX (20th Anniversary Special Edition)', 'artist_id': 220, 'artist_name': 'Rage Against The Machine', 'track_share_url': 'https://www.musixmatch.com/lyrics/Rage-Against-the-Machine/Bombtrack-Remastered?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'track_edit_url': 'https://www.musixmatch.com/lyrics/Rage-Against-the-Machine/Bombtrack-Remastered/edit?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'restricted': 0, 'updated_time': '2021-01-22T14:19:18Z', 'primary_genres': {'music_genre_list': [{'music_genre': {'music_genre_id': 1153, 'music_genre_parent_id': 21, 'music_genre_name': 'Heavy Metal', 'music_genre_name_extended': 'Rock / Heavy Metal', 'music_genre_vanity': 'Rock-Heavy-Metal'}}, {'music_genre': {'music_genre_id': 20, 'music_genre_parent_id': 34, 'music_genre_name': 'Alternative', 'music_genre_name_extended': 'Alternative', 'music_genre_vanity': 'Alternative'}}]}}}
{'name': 'Paul Oakenfold', 'artist_name': 'Paul Oakenfold feat. Andy Gray', 'track_name': 'Tast-E (Vocal mix)', 'track_id': 3380755, 'source': {'track_id': 3380755, 'track_name': 'Tast-E (Vocal mix)', 'track_name_translation_list': [], 'track_rating': 1, 'commontrack_id': 11011187, 'instrumental': 1, 'explicit': 0, 'has_lyrics': 0, 'has_subtitles': 0, 'has_richsync': 0, 'num_favourite': 0, 'album_id': 10496126, 'album_name': 'Tast-E', 'artist_id': 33588096, 'artist_name': 'Paul Oakenfold feat. Andy Gray', 'track_share_url': 'https://www.musixmatch.com/lyrics/Paul-Oakenfold-feat-Andy-Gray/Tast-E-Vocal-mix?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'track_edit_url': 'https://www.musixmatch.com/lyrics/Paul-Oakenfold-feat-Andy-Gray/Tast-E-Vocal-mix/edit?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'restricted': 0, 'updated_time': '2017-05-14T07:42:21Z', 'primary_genres': {'music_genre_list': []}}}
{'name': 'Paul Oakenfold', 'artist_name': 'Paul Oakenfold feat. Plump DJs', 'track_name': 'Ready Steady Go! - Plump DJs 303Bass Radio Edit', 'track_id': 91250766, 'source': {'track_id': 91250766, 'track_name': 'Ready Steady Go! - Plump DJs 303Bass Radio Edit', 'track_name_translation_list': [], 'track_rating': 37, 'commontrack_id': 39817028, 'instrumental': 0, 'explicit': 0, 'has_lyrics': 1, 'has_subtitles': 0, 'has_richsync': 0, 'num_favourite': 0, 'album_id': 21338908, 'album_name': 'Trance Mission', 'artist_id': 51610720, 'artist_name': 'Paul Oakenfold feat. Plump DJs', 'track_share_url': 'https://www.musixmatch.com/lyrics/Paul-Oakenfold/Ready-Steady-Go-Plump-DJs-303Bass-Radio-Edit?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'track_edit_url': 'https://www.musixmatch.com/lyrics/Paul-Oakenfold/Ready-Steady-Go-Plump-DJs-303Bass-Radio-Edit/edit?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'restricted': 0, 'updated_time': '2021-11-19T03:27:10Z', 'primary_genres': {'music_genre_list': [{'music_genre': {'music_genre_id': 1051, 'music_genre_parent_id': 17, 'music_genre_name': 'Trance', 'music_genre_name_extended': 'Dance / Trance', 'music_genre_vanity': 'Dance-Trance'}}]}}}
{'name': 'MC Supernatural', 'artist_name': 'Supernatural', 'track_name': 'Code Red', 'track_id': 100243787, 'source': {'track_id': 100243787, 'track_name': 'Code Red', 'track_name_translation_list': [], 'track_rating': 1, 'commontrack_id': 675156, 'instrumental': 0, 'explicit': 0, 'has_lyrics': 1, 'has_subtitles': 0, 'has_richsync': 0, 'num_favourite': 0, 'album_id': 22082329, 'album_name': 'Dreamcatcher', 'artist_id': 5110, 'artist_name': 'Supernatural', 'track_share_url': 'https://www.musixmatch.com/lyrics/Supernatural/Code-Red?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'track_edit_url': 'https://www.musixmatch.com/lyrics/Supernatural/Code-Red/edit?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'restricted': 0, 'updated_time': '2011-06-10T16:50:28Z', 'primary_genres': {'music_genre_list': [{'music_genre': {'music_genre_id': 15, 'music_genre_parent_id': 34, 'music_genre_name': 'R&B/Soul', 'music_genre_name_extended': 'R&B/Soul', 'music_genre_vanity': 'R-B-Soul'}}]}}}
{'name': 'MC Supernatural', 'artist_name': 'will.i.am feat. MC Supernatural', 'track_name': 'B Boyz', 'track_id': 162009356, 'source': {'track_id': 162009356, 'track_name': 'B Boyz', 'track_name_translation_list': [], 'track_rating': 14, 'commontrack_id': 12571862, 'instrumental': 0, 'explicit': 0, 'has_lyrics': 1, 'has_subtitles': 1, 'has_richsync': 0, 'num_favourite': 0, 'album_id': 30810798, 'album_name': 'Must B 21 (Soundtrack to Get Things Started)', 'artist_id': 13963767, 'artist_name': 'will.i.am feat. MC Supernatural', 'track_share_url': 'https://www.musixmatch.com/lyrics/will-i-am-feat-MC-Supernatural/B-Boyz?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'track_edit_url': 'https://www.musixmatch.com/lyrics/will-i-am-feat-MC-Supernatural/B-Boyz/edit?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'restricted': 0, 'updated_time': '2020-06-24T08:13:12Z', 'primary_genres': {'music_genre_list': [{'music_genre': {'music_genre_id': 18, 'music_genre_parent_id': 34, 'music_genre_name': 'Hip Hop/Rap', 'music_genre_name_extended': 'Hip-Hop/Rap', 'music_genre_vanity': 'Hip-Hop-Rap'}}]}}}
{'name': 'MC Supernatural', 'artist_name': 'Supernatural', 'track_name': 'Life Is A Miracle', 'track_id': 100243790, 'source': {'track_id': 100243790, 'track_name': 'Life Is A Miracle', 'track_name_translation_list': [], 'track_rating': 5, 'commontrack_id': 675176, 'instrumental': 0, 'explicit': 0, 'has_lyrics': 1, 'has_subtitles': 0, 'has_richsync': 0, 'num_favourite': 0, 'album_id': 22082329, 'album_name': 'Dreamcatcher', 'artist_id': 5110, 'artist_name': 'Supernatural', 'track_share_url': 'https://www.musixmatch.com/lyrics/Supernatural/Life-Is-a-Miracle?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'track_edit_url': 'https://www.musixmatch.com/lyrics/Supernatural/Life-Is-a-Miracle/edit?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'restricted': 0, 'updated_time': '2011-06-10T16:50:28Z', 'primary_genres': {'music_genre_list': [{'music_genre': {'music_genre_id': 15, 'music_genre_parent_id': 34, 'music_genre_name': 'R&B/Soul', 'music_genre_name_extended': 'R&B/Soul', 'music_genre_vanity': 'R-B-Soul'}}]}}}
{'name': 'MC Supernatural', 'artist_name': 'Supernatural', 'track_name': 'Rock U (Original Radio Edit)', 'track_id': 67122507, 'source': {'track_id': 67122507, 'track_name': 'Rock U (Original Radio Edit)', 'track_name_translation_list': [], 'track_rating': 1, 'commontrack_id': 35823881, 'instrumental': 0, 'explicit': 1, 'has_lyrics': 1, 'has_subtitles': 0, 'has_richsync': 0, 'num_favourite': 0, 'album_id': 18818761, 'album_name': 'Rock U (Remixes)', 'artist_id': 5110, 'artist_name': 'Supernatural', 'track_share_url': 'https://www.musixmatch.com/lyrics/Supernatural/Rock-U-Original-Radio-Edit?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'track_edit_url': 'https://www.musixmatch.com/lyrics/Supernatural/Rock-U-Original-Radio-Edit/edit?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'restricted': 0, 'updated_time': '2017-02-12T04:50:02Z', 'primary_genres': {'music_genre_list': [{'music_genre': {'music_genre_id': 15, 'music_genre_parent_id': 34, 'music_genre_name': 'R&B/Soul', 'music_genre_name_extended': 'R&B/Soul', 'music_genre_vanity': 'R-B-Soul'}}]}}}
{'name': 'MC Supernatural', 'artist_name': 'Supernatural', 'track_name': 'Kryptonite', 'track_id': 100243782, 'source': {'track_id': 100243782, 'track_name': 'Kryptonite', 'track_name_translation_list': [], 'track_rating': 4, 'commontrack_id': 675175, 'instrumental': 0, 'explicit': 0, 'has_lyrics': 1, 'has_subtitles': 1, 'has_richsync': 0, 'num_favourite': 0, 'album_id': 22082329, 'album_name': 'Dreamcatcher', 'artist_id': 5110, 'artist_name': 'Supernatural', 'track_share_url': 'https://www.musixmatch.com/lyrics/Supernatural/Kryptonite?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'track_edit_url': 'https://www.musixmatch.com/lyrics/Supernatural/Kryptonite/edit?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'restricted': 0, 'updated_time': '2011-06-10T16:50:28Z', 'primary_genres': {'music_genre_list': [{'music_genre': {'music_genre_id': 15, 'music_genre_parent_id': 34, 'music_genre_name': 'R&B/Soul', 'music_genre_name_extended': 'R&B/Soul', 'music_genre_vanity': 'R-B-Soul'}}]}}}
{'name': 'Iggy Pop', 'artist_name': '布袋寅泰 feat. Iggy Pop', 'track_name': 'How the Cookie Crumbles (2016 Remastered)', 'track_id': 118348600, 'source': {'track_id': 118348600, 'track_name': 'How the Cookie Crumbles (2016 Remastered)', 'track_name_translation_list': [{'track_name_translation': {'language': 'JA', 'translation': 'How The Cookie Crumbles (feat. イギー・ポップ)'}}], 'track_rating': 1, 'commontrack_id': 65331491, 'instrumental': 0, 'explicit': 0, 'has_lyrics': 1, 'has_subtitles': 0, 'has_richsync': 0, 'num_favourite': 0, 'album_id': 24447815, 'album_name': '51 Emotions - The Best For the Future', 'artist_id': 27915454, 'artist_name': '布袋寅泰 feat. Iggy Pop', 'track_share_url': 'https://www.musixmatch.com/lyrics/%E5%B8%83%E8%A2%8B%E5%AF%85%E6%B3%B0-feat-Iggy-Pop/How-the-Cookie-Crumbles-2016-Remastered?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'track_edit_url': 'https://www.musixmatch.com/lyrics/%E5%B8%83%E8%A2%8B%E5%AF%85%E6%B3%B0-feat-Iggy-Pop/How-the-Cookie-Crumbles-2016-Remastered/edit?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'restricted': 0, 'updated_time': '2020-02-21T13:14:14Z', 'primary_genres': {'music_genre_list': [{'music_genre': {'music_genre_id': 21, 'music_genre_parent_id': 34, 'music_genre_name': 'Rock', 'music_genre_name_extended': 'Rock', 'music_genre_vanity': 'Rock'}}]}}}
{'name': 'Iggy Pop', 'artist_name': 'Alani feat. Iggy Pop', 'track_name': 'Bitch', 'track_id': 188738223, 'source': {'track_id': 188738223, 'track_name': 'Bitch', 'track_name_translation_list': [], 'track_rating': 1, 'commontrack_id': 104013232, 'instrumental': 0, 'explicit': 1, 'has_lyrics': 1, 'has_subtitles': 1, 'has_richsync': 0, 'num_favourite': 0, 'album_id': 35907223, 'album_name': 'Bitch', 'artist_id': 42257038, 'artist_name': 'Alani feat. Iggy Pop', 'track_share_url': 'https://www.musixmatch.com/lyrics/Alani-Iggy-Pop/Bitch?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'track_edit_url': 'https://www.musixmatch.com/lyrics/Alani-Iggy-Pop/Bitch/edit?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'restricted': 0, 'updated_time': '2020-01-21T14:21:09Z', 'primary_genres': {'music_genre_list': [{'music_genre': {'music_genre_id': 6, 'music_genre_parent_id': 34, 'music_genre_name': 'Country', 'music_genre_name_extended': 'Country', 'music_genre_vanity': 'Country'}}]}}}
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{'name': 'Charli XCX', 'artist_name': 'Charli XCX feat. Hackman', 'track_name': 'Nuclear Seasons - Hackman Remix', 'track_id': 86092428, 'source': {'track_id': 86092428, 'track_name': 'Nuclear Seasons - Hackman Remix', 'track_name_translation_list': [], 'track_rating': 25, 'commontrack_id': 12094080, 'instrumental': 0, 'explicit': 0, 'has_lyrics': 1, 'has_subtitles': 0, 'has_richsync': 0, 'num_favourite': 1, 'album_id': 21015357, 'album_name': 'True Romance (Deluxe)', 'artist_id': 52254328, 'artist_name': 'Charli XCX feat. Hackman', 'track_share_url': 'https://www.musixmatch.com/lyrics/Charli-XCX/Nuclear-Seasons-Hackman-remix?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'track_edit_url': 'https://www.musixmatch.com/lyrics/Charli-XCX/Nuclear-Seasons-Hackman-remix/edit?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'restricted': 0, 'updated_time': '2024-01-11T16:51:31Z', 'primary_genres': {'music_genre_list': []}}}
{'name': 'Charli XCX', 'artist_name': 'Charli XCX feat. Troye Sivan & Montell2099', 'track_name': '2099 (feat. Troye Sivan) - Montell2099 Remix', 'track_id': 189260076, 'source': {'track_id': 189260076, 'track_name': '2099 (feat. Troye Sivan) - Montell2099 Remix', 'track_name_translation_list': [], 'track_rating': 40, 'commontrack_id': 104564910, 'instrumental': 0, 'explicit': 0, 'has_lyrics': 1, 'has_subtitles': 0, 'has_richsync': 0, 'num_favourite': 0, 'album_id': 36020688, 'album_name': '2099 (feat. Troye Sivan) [Montell2099 Remix]', 'artist_id': 42524866, 'artist_name': 'Charli XCX feat. Troye Sivan & Montell2099', 'track_share_url': 'https://www.musixmatch.com/lyrics/Charli-XCX-Troye-Sivan-Montell2099/2099-Troye-Sivan-Montell2099-Remix?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'track_edit_url': 'https://www.musixmatch.com/lyrics/Charli-XCX-Troye-Sivan-Montell2099/2099-Troye-Sivan-Montell2099-Remix/edit?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'restricted': 0, 'updated_time': '2023-12-09T15:25:45Z', 'primary_genres': {'music_genre_list': [{'music_genre': {'music_genre_id': 14, 'music_genre_parent_id': 34, 'music_genre_name': 'Pop', 'music_genre_name_extended': 'Pop', 'music_genre_vanity': 'Pop'}}]}}}
{'name': 'GloRilla', 'artist_name': 'GloRilla feat. Mike WiLL Made-It & CMG The Label', 'track_name': 'Pop It (GloRilla, Mike WiLL Made-It)', 'track_id': 262903803, 'source': {'track_id': 262903803, 'track_name': 'Pop It (GloRilla, Mike WiLL Made-It)', 'track_name_translation_list': [], 'track_rating': 16, 'commontrack_id': 163522098, 'instrumental': 0, 'explicit': 1, 'has_lyrics': 1, 'has_subtitles': 1, 'has_richsync': 0, 'num_favourite': 0, 'album_id': 58903778, 'album_name': 'Pop It (GloRilla, Mike WiLL Made-It)', 'artist_id': 57247116, 'artist_name': 'GloRilla feat. Mike WiLL Made-It & CMG The Label', 'track_share_url': 'https://www.musixmatch.com/lyrics/GloRilla-CMG-The-Label/Pop-It-GloRilla?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'track_edit_url': 'https://www.musixmatch.com/lyrics/GloRilla-CMG-The-Label/Pop-It-GloRilla/edit?utm_source=application&utm_campaign=api&utm_medium=Technical+University+of+Denmark%3A1409624530551', 'restricted': 0, 'updated_time': '2024-03-25T19:47:09Z', 'primary_genres': {'music_genre_list': [{'music_genre': {'music_genre_id': 18, 'music_genre_parent_id': 34, 'music_genre_name': 'Hip Hop/Rap', 'music_genre_name_extended': 'Hip-Hop/Rap', 'music_genre_vanity': 'Hip-Hop-Rap'}}]}}}

429
In [ ]:
lyrics = []
# Search each artist from the main stage for their top 5 tracks, parse response
mm_url = f"http://api.musixmatch.com/ws/1.1/track.lyrics.get"
mm_track_data = pd.read_csv("data/mm_track_metadata.csv")
 # songs where the artist does not match the searched artist
track_list = mm_track_data['track_id'].to_list()
for track_id in track_list:
    print(f"Searching: {track_id}")
    params = {"apikey": mm_access_token,
              "track_id": track_id}
    response = requests.get(mm_url, params=params)
    if not response.json()['message']['body']: # check for data
       print("Skipping")
       continue

    l = response.json()['message']['body']['lyrics']
    row = {"track_id": track_id,
           "text": l['lyrics_body'],
           "explicit": l['explicit'],
           "script_tracking_url": l["script_tracking_url"]}
    lyrics.append(row)

Searching: 35147897
Searching: 178403480
Searching: 43666506
Searching: 179156467
Searching: 976849
Searching: 63213649
Searching: 43508601
Searching: 558675
Searching: 88252574
Searching: 78167658
Searching: 2113579
Searching: 32226546
Searching: 84747864
Searching: 15089146
Searching: 17819242
Searching: 94825081
Searching: 4391000
Searching: 7578591
Searching: 7953467
Searching: 116534616
Searching: 215076354
Searching: 96336000
Searching: 89155388
Searching: 123705687
Searching: 87140017
Searching: 188319447
Searching: 187795063
Searching: 188323433
Searching: 4123728
Searching: 34202646
Searching: 84607892
Searching: 91220757
Searching: 93334925
Searching: 3383716
Searching: 93334901
In [ ]:
lyrics_df = pd.DataFrame(lyrics)
lyrics_df = lyrics_df.drop_duplicates(subset='track_id')
In [ ]:
artist_data = pd.read_csv("data/coachella_artists_full.csv")
In [ ]:
# Adding some of the artist data (artist_name, artist_genres, artist_id) to the track dataset, formatting the table
full_track_data = lyrics_df.merge(mm_track_data, on='track_id', how='left')
full_track_data = full_track_data.drop('Unnamed: 0', axis=1)
full_track_data = full_track_data.drop('artist_name', axis=1)
full_track_data = full_track_data.merge(artist_data[['name', 'id', 'genres']], on='name', how='left')
full_track_data = full_track_data.rename(columns={'name': 'artist_name',
                                            'id': 'artist_id',
                                            'genres': 'artist_genres'})
full_track_data = full_track_data.drop_duplicates(subset='track_id').reset_index()
full_track_data = full_track_data.drop('index', axis=1)
In [ ]:
# Save results
full_track_data.to_csv('data/song_lyrics_dataset.csv')
In [ ]:
# Import data  
artist_data = pd.read_csv("data/coachella_artists.csv")
song_data = pd.read_csv("data/song_dataset_2.csv")
lyric_data = pd.read_csv("data/lyrics_dataset.csv")

# Data set with genres 
artist_data = pd.read_csv("data/coachella_artists_full.csv")
Out[ ]:
Unnamed: 0 name id stage year genres followers popularity
0 0 Beck 4obzFoKoKRHIphyHzJ35G3 Main Stage 1999 ['latin pop', 'latin viral pop', 'rap latina',... {'href': None, 'total': 14417880} 75
1 1 Beck 4obzFoKoKRHIphyHzJ35G3 Gobi Tent 2004 ['latin pop', 'latin viral pop', 'rap latina',... {'href': None, 'total': 14417880} 75
2 2 Beck 4obzFoKoKRHIphyHzJ35G3 Coachella Stage 2014 ['latin pop', 'latin viral pop', 'rap latina',... {'href': None, 'total': 14417880} 75
3 3 The Chemical Brothers 1GhPHrq36VKCY3ucVaZCfo Main Stage 1999 ['alternative dance', 'big beat', 'breakbeat',... {'href': None, 'total': 2041904} 58
4 4 The Chemical Brothers 1GhPHrq36VKCY3ucVaZCfo Insomniac Tent 2001 ['alternative dance', 'big beat', 'breakbeat',... {'href': None, 'total': 2041904} 58
... ... ... ... ... ... ... ... ...
2907 2907 Cassian 1ChtRJ3f4rbv4vtz87i6CD Yuma Tent 2023 ['melodic house'] {'href': None, 'total': 53607} 50
2908 2908 TSHA 2kLa7JZu4Ijdz1Gle2khZh Yuma Tent 2023 ['uk house'] {'href': None, 'total': 70743} 46
2909 2909 LP Giobbi 3oKnyRhYWzNsTiss5n4Z1J Yuma Tent 2023 ['house'] {'href': None, 'total': 72986} 58
2910 2910 Airrica 57sPl5iWgq5t6AscVbMTOW Yuma Tent 2023 [] {'href': None, 'total': 853} 10
2911 2911 Minus the Light 2QKPp6hTeYMCSemGtfAmXx Yuma Tent 2023 [] {'href': None, 'total': 191} 1

2912 rows × 8 columns

Section 2: Basic Stats¶

Data Cleaning & Preprocessing¶

The Artist Dataset did not need any cleaning or preprocessing. It was important that we did not clean our dataset to get rid of duplicates, because one of the main aspects of our analysis was seeing if artists performed multiple times, and seeing which stage they returned to perform on.

The Song Dataset needed some cleaning up. This included removing all punctuation, stopwords, numbers, and the commercial use copyright tag generated from the MusixMatch API. We also added a few variables from the artist dataset (artist id, artist name, genres) to make matching the datasets easier later on. The final dataset also includes two types of cleaned lyrics, one with stopwords and one without stopwords, the sentiment of the song, and the sentiment score of each song. The raw lyric data can be found in data/song_lyrics_dataset.csv and the cleaned + preprocessed dataset can be found in data/lyrics_dataset.csv.

The preprocessing steps are shown below.

In [ ]:
full_track_data = pd.read_csv("data/song_lyrics_dataset.csv")

These two functions clean the raw lyric data. The only difference between the two is the clean_lyrics does not remove stopwords, while clean_lyrics_ws removes stopwords.

In [ ]:
def clean_lyrics(text):
    # Convert text to lowercase
    text = text.lower()

    # Remove commercial use flag from API
    if text.find("*******") != -1:
        text = text[:text.index("*******")]

    # Remove punctuation
    text = text.translate(str.maketrans('', '', string.punctuation))
    
    # Remove numbers
    text = re.sub(r'\d+', '', text)
    return text

# Clean lyrics including stopword removal
def clean_lyrics_ws(text):
    # Remove stopwords
    text = clean_lyrics(text)
    stop_words = set(stopwords.words('english'))
    text = ' '.join(word for word in text.split() if word not in stop_words)
    return text
In [ ]:
# Create two new columns for the two cleaned lyric sets
full_track_data['cleaned_lyrics'] = full_track_data['text'].apply(clean_lyrics)
full_track_data['cleaned_lyrics_ws'] = full_track_data['text'].apply(clean_lyrics_ws)

These two functions are the last step in preprocessing the lyric data, calculating the sentiment.

  • get_sentiment_score returns the total sentiment score for an entire song, summing the scores of each sentence using a TextBlob.
  • analyze_sentiment_lyrics determines the sentiment by tallying the number of lines that have a positive or negative score. If the song has more positive than negative lines, the function returns 'positive'. If the number of positives and negatives match, the function returns 'neutral'.
In [ ]:
def get_sentiment_score(lyrics):
    blob = TextBlob(lyrics) 
    total_song_sentiment = 0

    for sentence in blob.sentences:
        # Perform sentiment analysis
        sentiment_score = sentence.sentiment.polarity
        total_song_sentiment += sentiment_score
    
    return total_song_sentiment

def analyze_sentiment_lyrics(lyrics):
    blob = TextBlob(lyrics)
    total_song_sentiment = 0

    # Initialize sentiment counters
    positive_count = 0
    negative_count = 0
    neutral_count = 0
    
    for sentence in blob.sentences:
        # Perform sentiment analysis
        sentiment_score = sentence.sentiment.polarity
        total_song_sentiment += sentiment_score
        # Classify the sentence as positive, negative, or neutral
        if sentiment_score > 0:
            positive_count += 1
        elif sentiment_score < 0:
            negative_count += 1
        else:
            neutral_count += 1
    
    # Determine overall sentiment based on counts
    if positive_count > negative_count:
        overall_sentiment = "Positive"
    elif negative_count > positive_count:
        overall_sentiment = "Negative"
    else:
        overall_sentiment = "Neutral"
    
    return overall_sentiment
In [ ]:
# Add two columns for the sentiment and the sentiment_score
full_track_data['sentiment'] = full_track_data['cleaned_lyrics'].apply(analyze_sentiment_lyrics)
full_track_data['sentiment_score'] = full_track_data['cleaned_lyrics'].apply(get_sentiment_score)
In [ ]:
# Save data to file
full_track_data.to_csv("data/lyrics_dataset.csv")

Dataset Stats¶

The artist dataset contains 2912 rows of artists, 428 KB, and has 7 variables. This includes data for every artist to perform at Coachella since its inception in 1999 including the which stage they performed on, which years they were invited, their popularity on Spotify, and their most applicable genres.

The song dataset contains 1292 rows of song lyrics, 2791 KB, and has 9 variables. This dataset includes the top 5 songs (as determined by the MusixMatch API) for each artist that performed on the Main Stage of Coachella and the lyrics of each song. There is also a processed version of this dataset that includes cleaned lyrics and sentiment analysis scores.

Section 3: Tools, Theory and Analysis¶

Text Processing¶

The text processing in this project includes wordclouds and analysis the genres for each of the ten Coachella stages/tents. This is done with the Python package "Wordcloud", to visualize the genres for each tent toidentify any trends. In addition, sentiment analysis using TextBlob and word cloud analysis was performed on the lyric dataset.

Network science and data analysis¶

For the network analysis, the average degree of each node was found. The maximum and the minimum degree was found as well, where the degree represents the number of links that each node has. In our case, this represents how many other aritsts that peformed on the same stage, the same year. The average artist degree, the max degree and the min degree that were found made sense. Some artist only performed once, and are therefore only linked to the artists from that specific year, on that specific stage/tent. Some artist however, or at least one, have performed several times, and are therefore linked to all the artists for all the stages they have perfomed at, for all years. The average degree suggests that the artists in the network are of moderate degree, and relatively few artists have a higher degree of connectivity. This degree analysis was implemented partly to assure that the network was created in the correct way, since it was known beforehand approximately how many artists that repeatedly performed at Coachella. Moreover, it is interesting to conclude from this that is is less common to perform several times, then to repeatedly perform. This was also confirmed by a degree distribution plot.

The number of connected components was conducted to see if the graph is connected. It could have been so that one group of artists, that performed on some stage one year would create a disconnected component from the other artists, if no artists out of these performed several times. However, it was found that this was not the case. The number of connected components in the graph was one.

In addition, community detection with the package "Community" was executed, and then the communitites from best_partition were analysed to see if any patterns in genre, year or stages were found. When analysing the best_partition communities, no visible patterns could be found with this analysis. The genres, years and stages are mixed in all 24 communities found. This is because the algorithm for finding the best partition optimizes the modularity coefficient, and the modularity coefficients for partitions based on genres, years or stages respectively, are not as high as for the communities from best_partition. The modularity coefficient for the best_partition communities is 0.72 (from code below), which implies a strong partition structure. This analysis part was implemented to see if the inbuilt model best_partition could find communities based on any of the attributes that were added to the graph.

In [ ]:
""" Function from exercises week 6 - creating graphs with authors and adding attibutes. """ 

def add_attributes(graph : nx.Graph, df : pd.DataFrame): 
    for node in tqdm(graph.nodes, desc="Adding atrributes"): 
        attributes = df[df["name"] == node]
        graph.add_node(node, year=attributes["year"].values)
        graph.add_node(node, stage=attributes["stage"].values)
        graph.add_node(node, genre=attributes["genres"].values)

""" Create a network of artists, with a link between them if they performed at the same stage, at the same year. """

def create_graph(dataset):
    artists_graph = nx.Graph()
    
    grouped_data = dataset.groupby(["year", "stage"])
    
    for (year, stage), group in tqdm(grouped_data, desc="Creating graph"):
        artists_on_stage = set(group["name"])
        artists_graph.add_nodes_from(artists_on_stage)
        
        # Add links
        for artist1 in artists_on_stage:
            for artist2 in artists_on_stage:
                if artist1 != artist2:
                    artists_graph.add_edge(artist1, artist2, year=year, stage=stage)
    
    return artists_graph
In [ ]:
# Create graph and add attributes 
artists_graph = create_graph(artist_data)
add_attributes(artists_graph, artist_data)

Creating graph: 100%|██████████| 124/124 [00:00<00:00, 766.50it/s]
Adding atrributes: 100%|██████████| 2240/2240 [00:01<00:00, 1722.26it/s]
In [ ]:
# Check that there are no duplicate artists in the graph 
if len(set(artists_graph.nodes())) == len(artists_graph.nodes()): 
    print("No duplicates found in artist graph")
else: 
    print("Duplicates found")

No duplicates found in artist graph
In [ ]:
# Visualize graph 
positions, b = nw.visualize(artists_graph)
In [ ]:
n, m = artists_graph.number_of_nodes(), artists_graph.number_of_edges()
fig, ax = nw.draw_netwulf(positions)
plt.title(f"Artist graph G with {n} nodes and {m} edges")
plt.savefig("data/Artist_graph.png")
plt.show()
No description has been provided for this image
In [ ]:
# Graph and data analysis

def degree_analysis(graph): 
    graph_degrees = graph.degree()
    degrees = [degree for author, degree in graph_degrees]
    sum_degrees = sum(degrees)

    average_degree = sum_degrees/len(graph_degrees)
    max_degree = max(degrees)
    min_degree = min(degrees)
    return average_degree, max_degree, min_degree

def plot_degree_distribution(graph): 
    degree_distribution = nx.degree_histogram(graph)

    plt.bar(range(len(degree_distribution)), degree_distribution)
    plt.xlabel("Degree")
    plt.ylabel("Number of artists")
    plt.title("Degree Distribution of G")
    plt.show()
In [ ]:
# Degree analysis

average_degree, max_degree, min_degree = degree_analysis(artists_graph)
print(f"The average artist degree is {round(average_degree,2)}")
print(f"The maximum degree is {(max_degree)}") 
print(f"The minimum degree is {(min_degree)}") 

plot_degree_distribution(artists_graph)

The average artist degree is 31.51
The maximum degree is 180
The minimum degree is 6
No description has been provided for this image
In [ ]:
# Find artists that performed more than n, and find top N artists

def find_duplicate_artists(df, n, N): 
    count = df.groupby("name").size()
    duplicates = count[count > n]
    print(f"{len(duplicates)} artists performed more than {n} times.")
    top_artists = duplicates.nlargest(N)
    print("\nTop artists: ")
    for artist, nr in top_artists.items():
        print(f"{artist} performed {nr} times.")
    return duplicates.index.tolist(), top_artists 

n_times = 2
dup_artists, top_artists = find_duplicate_artists(artist_data, n_times, 5)

151 artists performed more than 2 times.

Top artists: 
Alf Alpha performed 7 times.
Gabe Real performed 7 times.
The Chemical Brothers performed 7 times.
Jimbo Jenkins performed 6 times.
Richie Hawtin performed 6 times.
In [ ]:
# Check if the graph is connected by calculating number of connected components

nr_con_comp = nx.number_connected_components(artists_graph)
if nr_con_comp == 1:
    print(f"Graph G is connected")
else: 
    print(f"Number of connected components in G are {nr_con_comp}")

Graph G is connected

Community detection¶

In [ ]:
import community
In [ ]:
# Find communintes based on comminuty.best_partition

communities_in_graph = community.best_partition(artists_graph)
nx.set_node_attributes(artists_graph, communities_in_graph, "group")

nr_communities = len(set(communities_in_graph.values()))
print(f"{nr_communities} communites found by best_partition")

25 communites found by best_partition
In [ ]:
communities_pos, config = nw.visualize(artists_graph)
In [ ]:
fig, ax = nw.draw_netwulf(communities_pos)
plt.title(f"Artist graph G with communities")
plt.savefig("data/Community_artist_graph.png")
plt.show()
No description has been provided for this image
In [ ]:
""" A function for analysing the communitites created with best_partition, based on attribute: stage, year or genre to see if there are any correlations.
The n top attributes for each community will be printed. """

def analyse_communities(communities, graph, attribute : str, n : int):
    for community_id in set(communities.values()):
        nodes_in_community = [node for node, community in communities.items() if community == community_id]
        community_attributes = {}
        for node in nodes_in_community:
            for attr in graph.nodes[node][attribute]:
                if attr not in community_attributes:
                    community_attributes[attr] = 0
                community_attributes[attr] += 1
        
        sorted_attributes = sorted(community_attributes.items(), key=lambda x: x[1], reverse=True)
        
        print(f"Community {community_id}:")
        for attr, f in sorted_attributes[:n]:
            print(f"{attr}")
In [ ]:
analyse_communities(communities_in_graph, artists_graph, "year", 3)

Community 0:
2012
2014
1999
Community 1:
2013
2010
2016
Community 2:
2007
2004
2006
Community 3:
2017
2012
2015
Community 4:
2012
2007
2015
Community 5:
2013
2008
2010
Community 6:
2013
2008
2010
Community 7:
2014
2016
2017
Community 8:
2013
2016
2019
Community 9:
2013
2014
2019
Community 10:
2015
2012
2017
Community 11:
2011
2015
2017
Community 12:
2022
2023
2017
Community 13:
2019
2023
2022
Community 14:
2009
2006
2001
Community 15:
2023
2015
2018
Community 16:
2022
2018
2016
Community 17:
2003
2009
1999
Community 18:
2023
2022
2019
Community 19:
2016
2019
2018
Community 20:
2011
2008
2002
Community 21:
2011
2009
2008
Community 22:
2014
2017
2004
Community 23:
2022
Community 24:
2022
2019
2017
In [ ]:
def calc_modularity_score(graph, communities):
    community_partitions = {}
    for node, community_id in communities.items():
        if community_id not in community_partitions:
            community_partitions[community_id] = set()
        community_partitions[community_id].add(node)

    partition_list = [set(nodes) for nodes in community_partitions.values()]

    M = nx.community.modularity(graph, partition_list)

    return M
In [ ]:
M = calc_modularity_score(artists_graph, communities_in_graph)
print(f"Modularity coefficient M for the best_partition communities is {round(M,2)}")

Modularity coefficient M for the best_partition communities is 0.72

Finding top artists in datasets¶

In [ ]:
# Read dataset with artists and songs combined 

songs_artists_df = pd.read_csv("data/songs_and_artists_dataset.csv")
In [ ]:
import ast
In [ ]:
""" A function for finding the top artist based on the n songs with the highest Spotify popularity score. 
To do this it is necessary to covert all artist from strings to list elements. It is also necessary to exclude the artist that have not performed at Coachella.
There might be some artists included in the dataset that did not perform at Coachella, since we gatherered the artists based on collaboration on songs. """

def get_top_unique_aritst(df, n):
    top_popularity_artists = df.nlargest(n, "popularity")["artist_names"].tolist()
    artist_names = []
    for item in top_popularity_artists:
        artist_names.extend(ast.literal_eval(item))

    unqiue_top_artists = ((set(artist_names)))

    # Make sure we only include Coachella artists
    top_coachella_artists = unqiue_top_artists & set(artist_data["name"])

    return top_coachella_artists
In [ ]:
n = 300 
top_coachella_artists = get_top_unique_aritst(songs_artists_df, n)

print(f"{len(top_coachella_artists)} top artists found based on the {n} top songs.\n")

print("Top artists:")
for a in (top_coachella_artists): 
    print(a)

98 top artists found based on the 300 top songs.

Top artists:
TV Girl
Foo Fighters
Eric Prydz
Corinne Bailey Rae
Cardi B
Mac DeMarco
Kanye West
Disclosure
Drake
UMI
Chase & Status
boygenius
Halsey
Phoebe Bridgers
Calvin Harris
Tom Odell
Mitski
Tame Impala
Dennis Lloyd
Ellie Goulding
Future
Bad Bunny
Zedd
Declan McKenna
Bomba Estéreo
Chris Stapleton
Alessia Cara
Anitta
G-Eazy
Peter Bjorn and John
A$AP Ferg
Tiësto
Natanael Cano
Jimmy Eat World
Sia
Flo Milli
Still Woozy
Ali Gatie
The Weeknd
21 Savage
Whethan
Kendrick Lamar
Kali Uchis
DJ Khaled
Kid Cudi
Amy Winehouse
Travis Scott
Gucci Mane
The Chainsmokers
Nicki Nicole
Lil Uzi Vert
Fred again..
Pavement
FKJ
Kim Petras
Doja Cat
Alec Benjamin
David Guetta
Mild High Club
Milky Chance
Joey Bada$$
Ariana Grande
Thundercat
Metro Boomin
Baby Keem
Aphex Twin
SZA
The Neighbourhood
Stromae
Playboi Carti
Dominic Fike
The Cinematic Orchestra
Oasis
Cults
Becky G
Daft Punk
AC/DC
Eliza Doolittle
Mariah the Scientist
Sasha Alex Sloan
Major Lazer
Julian Casablancas
Lil Baby
Ari Lennox
Mareux
Mark Ronson
Tyler, The Creator
Masego
Marshmello
Lorde
Sam Feldt
The Shins
Miguel
Khalid
Crystal Castles
Frank Ocean
Ms. Lauryn Hill
J Balvin
In [ ]:
f""" Is there a correlation between the popularity of the Coachella artist (based on popularity score), 
and how many times they have performed at Coachella? This is done by calculating how many of the {len(top_coachella_artists)} that have performed more than {n_times}."""

correlation_list = list(set(dup_artists).intersection(top_coachella_artists))
print(f"{len(correlation_list)} of the top artists performed more than {n_times} times at Coachella.\n")

print("These artists are:")
for i, a in enumerate(correlation_list):
    print(f"{i+1}:", a)

22 of the top artists performed more than 2 times at Coachella.

These artists are:
1: Zedd
2: Mac DeMarco
3: Flume
4: Queens of the Stone Age
5: Tiësto
6: Disclosure
7: Major Lazer
8: Vince Staples
9: Sia
10: Benny Benassi
11: Vampire Weekend
12: David Guetta
13: Calvin Harris
14: Tame Impala
15: The Weeknd
16: Diplo
17: Ellie Goulding
18: MGMT
19: Yeah Yeah Yeahs
20: Crystal Castles
21: Justice
22: Ratatat
In [ ]:
""" Here analysis of the artists that have performed several times is made, by finding the stage they perform at each year, 
and checking if they have advanced from a non-Coachella stage to a Coachella stage (which is the main stage). 
If they have advanced, it indicates that their popularity has risen. """

proceeding_artists, other_artists = 0, 0
always_popular_artists, regressing_artists = 0, 0 

names_stages = {}
for name in dup_artists: 
    filtered_df = artist_data[artist_data["name"] == name]
    stages = filtered_df['stage'].tolist()
    years = filtered_df['year'].tolist()
    if stages[-1] == "Coachella Stage" and stages[0] != "Coachella Stage":
        proceeding_artists += 1
    elif all(item == "Coachella Stage" for item in stages):
        always_popular_artists += 1
    elif stages[0] == "Coachella Stage" and stages[-1] != "Coachella Stage":
        regressing_artists += 1 
    else: 
        other_artists += 1

print(f"{proceeding_artists} out of the {len(dup_artists)} artists started out on the non main stages, and eventually proceeded to the Coachella stage.")
print(f"{always_popular_artists} artists performed on the Coachella stage all times.")
print(f"{regressing_artists} artists started out on the Coachella stage on their first peformance, and then regressed to another stage.")

44 out of the 151 artists started out on the non main stages, and eventually proceeded to the Coachella stage.
7 artists performed on the Coachella stage all times.
7 artists started out on the Coachella stage on their first peformance, and then regressed to another stage.

So, there are 151 artists that performed at Coachella more than 2 times from 1999 to 2023. One can conclude that 44 out of these 151 artists started out on the non main stages, and eventually proceeded to the Coachella stage. 7 artists performed on the Coachella stage all times, and 7 artists started out on the Coachella stage on their first peformance, and then regressed to another stage. When looking more into detail to the artists and their respective stages, one can tell that some very popular artists (as David Guetta for example) never performed on the Cochella Stage despite this popularity. This means that this is not a perfect way of measuring popularity.

This analysis is limited to only taken into account the first and the last stage they performed at (if they did not always perform at the Coachella Stage). That is another limitation with using this specific way of measuring popularity.

Find the top genres of each stage¶

In [ ]:
# Starting out with finding the names of each unique stage/tent

stages = set(artist_data["stage"].tolist())

print(f"There are {len(stages)} possible stages at Coachella")
for i, st in enumerate(stages):
    print(f"{i+1}: {st}")

There are 10 possible stages at Coachella
1: Gobi Tent
2: Insomniac Tent
3: Sahara Tent
4: Main Stage
5: Coachella Stage
6: Outdoor Theatre
7: Mojave Tent
8: Oasis Dome
9: Sonora Tent
10: Yuma Tent
In [ ]:
from collections import Counter
In [ ]:
# Find the n_genres top genres for each stage, each year

def top_n_genres(row : list, n : int):
    flattened_list = [item for sublist in row for item in sublist]
    element_counts = Counter(flattened_list).most_common(n)
    top_elem = [e for (e, c) in element_counts if len(e) != 0]
    return top_elem

def convert_str_to_list(row : str): 
    try: 
        str_row = ast.literal_eval(row)
        return str_row
    except: 
        return ast.literal_eval(row)
    
def find_top_genres(df : pd.DataFrame, n : int):
    df_lists = df.copy()
    
    # Convert string to list items 
    df_lists["genres"] = df["genres"].apply(lambda row: convert_str_to_list(row))

    grouped_artist_data = df_lists.groupby(["year", "stage"])["genres"].agg(list).reset_index()
    grouped_artist_data["top_genres"] = grouped_artist_data["genres"].apply(lambda row: top_n_genres(row, n))
    grouped_artist_data = grouped_artist_data.drop(columns=["genres"])
    return grouped_artist_data

def find_top_all_stages(df : pd.DataFrame, n : int): 
    df_alL_stages = df.copy()
    df_alL_stages["top_genres"] = df_alL_stages["top_genres"]
    return df_alL_stages
In [ ]:
# Top genres for each year, each tent

n_genres = 3
top_genres_df = find_top_genres(artist_data, n_genres)
top_genres_df
Out[ ]:
year stage top_genres
0 1999 Gobi Tent [trip hop, intelligent dance music, electronica]
1 1999 Main Stage [alternative rock, alternative metal, nu metal]
2 1999 Mojave Tent [trip hop, electronica, downtempo]
3 1999 Outdoor Theatre [electronica, alternative rock, britpop]
4 1999 Sahara Tent [detroit techno, minimal techno, techno]
... ... ... ...
119 2023 Mojave Tent [indie pop, art pop, metropopolis]
120 2023 Outdoor Theatre [indie soul, rap, electropop]
121 2023 Sahara Tent [house, bass house, minimal techno]
122 2023 Sonora Tent [crank wave, pov: indie, alternative r&b]
123 2023 Yuma Tent [house, melodic house, minimal techno]

124 rows × 3 columns

In [ ]:
# Find top genres for each stage, all years 

all_top_genres_stage_df = top_genres_df.groupby(["stage"])["top_genres"].agg(list).reset_index()

top_genres_stage_df = all_top_genres_stage_df.copy()

top_genres_stage_df["top_genres"] = all_top_genres_stage_df["top_genres"].apply(lambda row: top_n_genres(row, n_genres))
top_genres_stage_df
Out[ ]:
stage top_genres
0 Coachella Stage [alternative rock, rock, modern rock]
1 Gobi Tent [indietronica, escape room, art pop]
2 Insomniac Tent [big beat, breakbeat, electronica]
3 Main Stage [alternative rock, alternative metal, nu metal]
4 Mojave Tent [indie rock, indietronica, electronica]
5 Oasis Dome [future garage, drum and bass, classic dubstep]
6 Outdoor Theatre [indie rock, indietronica, alternative rock]
7 Sahara Tent [edm, electro house, pop dance]
8 Sonora Tent [indie garage rock, noise pop, art pop]
9 Yuma Tent [minimal techno, microhouse, house]
In [ ]:
# Save dataframes with top artists, both per stage and stage per year 

top_genres_df.to_csv("data/top_genres_stage_year.csv")
top_genres_stage_df.to_csv("data/top_genres_stage.csv")

Make wordclouds of the genre names for each stage¶

Now we want to analyse the different genres of the artist on each of the ten stages. There are many different genres as "neo", "escape room", "brooklyn" etc, and it can be interesting to collect the genres for each stage together and visualise it as a wordcloud, to identifying eventual patterns.

In [ ]:
from wordcloud import WordCloud
In [ ]:
def plot_genre_wordclouds(df):
    for ind, df_row in df.iterrows():
        stage = df_row["stage"]
        flattened_genres = [item for sublist in df_row["top_genres"] for item in sublist]
        genres_str = ', '.join(flattened_genres)
        wordcloud = WordCloud(width=800, height=400, background_color="white").generate(genres_str)
        
        plt.figure(figsize=(12, 3))
        plt.imshow(wordcloud)
        plt.title(f"Wordcloud based on genres from {stage}")
        plt.show()
In [ ]:
plot_genre_wordclouds(all_top_genres_stage_df)
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Lyric Analysis¶

Sentiment Analysis¶

In this section we will be analyzing the sentiment scores from the lyrics_dataset.csv file. It is important to note that all songs in this dataset come from performers that performed on the Main Stage of Coachella, so we cannot draw any conclusions on the festival as a whole, just the Main Stage. This is simply a limitation of the amount of textual data we could collect without a subscription to any lyric API.

Sentiment Analysis Results¶

Below you will find histograms with the results of our sentiment analysis.

The first histogram shows the total sentiment scores of every song in the dataset. A positive score means a positive sentiment and a negative score means a negative sentiment. We can see that there is a slight majority of songs that have a positive sentiment and the plot roughly follows a normal distribution. We expected songs to have more extreme sentiments, that is, there would be more songs that have large positive values or large negative values. Instead, it seems that most songs have relatively neutral sentiments, with few having strong positive or negative sentiments.

In [ ]:
# Combine year data with the track data for analysis
right = artist_data[['id', 'year']].rename(columns={'id': 'artist_id'})
tracks_with_years = full_track_data.merge(right, on='artist_id', how='left')
sentiment_with_year = tracks_with_years[['artist_id', 'sentiment']]
sentiment_with_year = sentiment_with_year.sort_values(['sentiment', 'year'])
In [ ]:
# Specify the number of bins for the histogram
num_bins = 20

# Plotting the histogram of sentiment scores with 10 bins
plt.figure(figsize=(8, 6))
plt.hist(full_track_data['sentiment_score'], bins=num_bins, edgecolor='black', alpha=0.7)

# Adding labels and title
plt.xlabel('Sentiment Score')
plt.ylabel('Frequency')
plt.title('Song Lyric Sentiment Scores')

# Display the plot
plt.grid(True)
plt.show()
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Here we have a histogram for each year of Coachella. With these graphs, we can compare the sentiments of artists who performed each year. Each histogram shows the number of songs associated with that year (by associated we mean that the artist who wrote the song performed at Coachella on that year) that fell into each sentiment analysis category. From initial inspection, we can see that most years are predominantly positive in sentiment. Out of the 22 years, 17 had a majority of positive sentiment songs.

While the overwhelming majority of years had an overall positive sentiment, it is not enough to conclude that any individual Coahella festival was particularly positive. Since the song/lyric data is not based off the set list of that individual performance, an artist at Coachella could've performed entirely negative songs while scoring mostly positive in this analysis. What these graphs do show is that performers that have been invited to play the Main Stage tend to have discographies with positive sentiments.

In [ ]:
unique_years = sorted(sentiment_with_year['year'].unique())

# Set up subplots (one subplot per year)
fig, axs = plt.subplots(nrows=len(unique_years), figsize=(5, 3 * len(unique_years)))

# Iterate over unique years
for i, year in enumerate(unique_years):
    # Filter data for the current year
    data_year = sentiment_with_year[sentiment_with_year['year'] == year]['sentiment']
    
    # Plot histogram for the current year
    axs[i].hist(data_year, bins=5, alpha=0.75, edgecolor='black')
    axs[i].set_title(f'Main Stage Sentiment Distribution - {year}')
    axs[i].set_xlabel('Sentiment Score')
    axs[i].set_ylabel('Frequency')
    axs[i].grid(False)

# Adjust layout and show the plot
plt.tight_layout()
plt.show()
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Lyrics Cloud Analysis¶

In addition to sentiment analysis, we wanted to generate word clouds to identify trends in lyrics in Main Stage performances. Below you can find word clouds for each year of the festival and all the way down, you will find a word cloud for all 22 years of the festival.

There are a few trends within these clouds. Firstly, there are clearly groups of associated words that appear frequently, such as the "love song" group. This includes words such as: love, baby, babe, sexy, heart, etc. and could also include helper words such as: get, got, or tonight. There is also the appearance of other languages, mainly Spanish. It is very easy to tell which years included hispanic performers due to the appearance of que, el, la and other Spanish stopwords that were not removed by the English filter.

Overall, there does not seem to be significant differences between the word clouds. It is difficult to spot trends in these word clouds as often the most interesting words appear only once while words that are common among traditional pop themes (ex: love songs) dominate the word clouds. This is not to say that the years are all the same but there are not many standout years.

In [ ]:
for i, year in enumerate(unique_years):
    # Initialize an empty list to store all tokens
    all_tokens = []
    texts = tracks_with_years[tracks_with_years['year'] == year]['cleaned_lyrics_ws'].to_list()
    # Tokenize, clean, and collect all tokens from each text
    for text in texts:
        # Tokenize the text into words
        tokens = word_tokenize(text.lower())
        # Removing some stopwords that were not caught in the initial filter
        filtered_tokens = [token for token in tokens if token not in ['im', 'like', 'dont', 'oh']]
        all_tokens.extend(filtered_tokens)

    # Calculate word frequencies using Counter from collections
    word_counts = Counter(all_tokens)

    # Create a WordCloud object and generate the word cloud from word counts
    wordcloud = WordCloud(width=800, height=400, background_color='white').generate_from_frequencies(word_counts)
    print(year)
    # Display the word cloud using matplotlib
    plt.figure(figsize=(10, 5))
    plt.imshow(wordcloud, interpolation='bilinear')
    plt.axis('off')  # Hide axis
    plt.show()

1999
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2001
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2002
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2003
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2004
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2005
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2006
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2007
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2008
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2009
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2010
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2011
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2012
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2013
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2014
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2015
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2016
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2017
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2018
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2019
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2022
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2023
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This final word cloud is a word cloud of all songs in the dataset. These are the most common words of artists that performed on the Main Stage in Coachella's 24 year history.

In [ ]:
# Repeat the same process but for all years
all_tokens = []
texts = tracks_with_years['cleaned_lyrics_ws'].to_list()
# Tokenize, clean, and collect all tokens from each text
for text in texts:
    # Tokenize the text into words
    tokens = word_tokenize(text.lower())
    # Removing some stopwords that were not caught in the initial filter
    filtered_tokens = [token for token in tokens if token not in ['im', 'like', 'dont', 'oh']]
    all_tokens.extend(filtered_tokens)

# Calculate word frequencies using Counter from collections
word_counts = Counter(all_tokens)

# Create a WordCloud object and generate the word cloud from word counts
wordcloud = WordCloud(width=800, height=400, background_color='white').generate_from_frequencies(word_counts)
# Display the word cloud using matplotlib
plt.figure(figsize=(10, 5))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')  # Hide axis
plt.show()

2023
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Section 4: Discussion¶

What went well?¶

A lot of time for this project was dedicated to collect the datasets, with Spotify and MusixMatch APIs. Originally, the plan was to do text analysis on the titles for the songs, since this was the data that was possible to gather from Spotify APIs. However, we realized that the text analysis would be to sparse with only the few words in the titles, so we managed to gather data from song lyrics from MusixMatch APIs. This was a successful part of the project, and made the text analysis part more comprehensive. In addition, considerable amount of the sought data was accessible, so the data cleaning and handling went well.

Furthermore, some trends were identified in the genres of each tent, which was a hypothesis of ours. For example, the genres of the Oasis Dome has genres in common that are more alternative, compared to the Sahara tent which is clearly more dance/party music.

Regarding the text analysis for the lyrics, we were able to get enough data to identify some trends using sentiment analysis. While the distribution of sentiments was not what we expected, we gained a lot of valuable information from the analsis. Additionally, we succeeded to compute semantic analysis, and see how the festival has changed since 1999. The idea of this project was to dig into the history of Coachella performances and examine the relationship between artists and the content of their music and while we did not find exactly what we expected, we learned a lot in the process.

Areas of improvment¶

Due to time limitations, we did not implement everything that we might have wanted to. For example, the network created is an undirected, unweighted network, and one can assume that it is non-random. This since the line up for each year of Coachella probably is carefully planned by the Coachella organisers, and the schedule for the artists (with different stages/tents) is most likely based on at least some criterias. However, we could have investigated whether or not the network has same characteristics as random networks. That is something that we worked with in this course, and it could have been interesting to apply to our project.

Another area of improvments is the measurement for the artist popularity over the years, which is now executed by checking the first and the last stage that artist performed at, if they have performed repeatedly at Coachella. This measurement could have been modified to capture the stage changes over the years in a more comprehensive way, perhaps with a plot.

For textual analysis, the limitations of free lyric APIs caused us a lot of problems. Initially we tried the Genius API, which would give us 100% of song lyrics. Unfortunately, the rate limit was way too low to get a usable amount of data so we had to switch to MusixMatch which only gives 30% of each song. Additionally, we had no way of identifying the songs artists performed on any given day. This meant that our song data could not be representative of the actual Coachella performance, so the best we could do was to analyze the popular songs of each performer. Ideally, we would have been able to find data on the set lists and songs, so we could analyze sentiment for each performance to better reflect the real event. This would allow us to explore deeper questions about the selection of music and the time of performance.